Tone quality adjusting method combining vacuum tube and TFT screen display

Abstract

The invention provides a tone quality adjustment method combining vacuum tube and TFT screen display, and relates to the technical field of audio processing, the tone quality adjustment method obtains spectrum optimization information through a vacuum tube module, preliminarily realizes tone quality processing, and uses a TFT screen to display initial tone quality parameter combination and distortion degree information in real time, so that a user can visually know the current tone quality state; physiological feedback data of the user is analyzed based on a deep learning model, a tone quality adjustment strategy dynamically adaptive to the user state is generated, tone quality parameter combination is further optimized, and the adjustment process is more intelligent; a user can modify tone quality parameters in real time on the interactive adjustment interface, and finally optimized secondary tone quality parameter combination indexes are generated and applied to digital sound equipment adjustment. The accuracy and personalization of tone quality adjustment are improved, and efficient real-time feedback and interactive experience are provided.

Description

Technical Field

[0001] The present invention relates to the technical field of audio processing, and specifically to a sound quality tuning method combining a vacuum tube and a TFT screen display. Background Art

[0002] With the popularization of audio playback devices, users' personalized demands for sound quality are constantly increasing. Traditional sound quality tuning methods mainly rely on preset sound effect modes and lack the ability to adjust in real time according to the individual physiological characteristics of users. The application of vacuum tubes in audio signal processing can provide unique timbre characteristics, but its optimization strategy is often fixed and cannot be dynamically adjusted according to the real-time state of users.

[0003] There are still many deficiencies in the existing sound quality tuning technologies in terms of achieving sound quality optimization and user interaction. First of all, traditional vacuum tube audio devices usually lack an intuitive user interface, and it is difficult for users to monitor and adjust various audio parameters in real time during sound quality tuning, resulting in a cumbersome tuning process and relying on experience.

[0004] Secondly, although the existing digital audio processing methods provide high-precision sound quality optimization capabilities, they often ignore the subjective auditory experience of users and lack a dynamic tuning mechanism based on users' physiological feedback. In addition, the display screens of many audio devices have a single function and can only present basic audio parameters, unable to achieve complex interactive operations and real-time feedback, which limits users' control and understanding of sound quality optimization;

[0005] The above information disclosed in the above background art section is only used to enhance the understanding of the background of the present disclosure, so it may include information that does not constitute the prior art known to those of ordinary skill in the art. Summary of the Invention

[0006] The purpose of the present invention is to provide a sound quality tuning method combining a vacuum tube and a TFT screen display to solve the problems raised in the above background art.

[0007] To achieve the above purpose, the present invention provides the following technical solutions:

[0008] A sound quality tuning method combining a vacuum tube and a TFT screen display, the specific steps include:

[0009] Step S1: Obtain the spectrum optimization information of the input audio signal by the vacuum tube module at the current moment to obtain an initial sound quality parameter combination;

[0010] Step S2: Respond to the spectrum optimization information, and use the TFT screen to display the initial sound quality parameter combination after spectrum optimization in real time, and at the same time display the distortion degree evaluated after adjustment based on the initial sound quality parameter combination;

[0011] Step S3: Collect the real-time physiological parameters of the client under different combinations of sound quality parameters and distortion degrees, and analyze this data through a deep learning model to generate a sound quality tuning strategy that adapts to the current initial combination of sound quality parameters and distortion degree based on the real-time physiological parameters, and obtain the adjusted first-level sound quality parameter combination index;

[0012] Step S4: Respond to the sound quality tuning strategy, and display the interactive tuning interface and the adjusted first-level sound quality parameter combination index through the TFT screen;

[0013] Step S5: Receive the operation information adjusted in real time by the client through the TFT screen interactive tuning interface, and finally adjust the first-level sound quality parameter combination index based on the operation information to generate the finally optimized second-level sound quality parameter combination index;

[0014] Step S6: Use the second-level sound quality parameter combination index to adjust the audio signal gain and frequency spectrum distribution of the digital audio device, and display the adjustment result of the sound quality parameter combination on the TFT screen.

[0015] Compared with the prior art, the beneficial effects of the present invention are as follows: Obtain spectrum optimization information through the vacuum tube module, initially realize sound quality processing, and use the TFT screen to display the initial sound quality parameter combination and distortion degree information in real time, so that users can intuitively understand the current sound quality state; Analyze the physiological feedback data of users through a deep learning model, generate a sound quality tuning strategy that dynamically adapts to the user's state, further optimize the sound quality parameter combination, and make the tuning process more intelligent; Users can modify the sound quality parameters in real time on the interactive tuning interface, generate the finally optimized second-level sound quality parameter combination index, and apply it to the tuning of digital audio devices; Improve the accuracy and personalization of sound quality tuning, and at the same time provide an efficient real-time feedback and interaction experience; Combining the data analysis of the user's physiological state, the present invention realizes high-quality and personalized sound quality optimization, providing an innovative path for the intelligent development of audio devices. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a schematic diagram of the overall method flow of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0017] In order to make the objectives, technical solutions and advantages of the present invention more clear and understandable, the present invention will be further described in detail below with reference to specific embodiments.

[0018] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the present invention should have the ordinary meanings understood by those with ordinary skills in the field to which the present invention belongs. The "first", "second" and similar terms used in the present invention do not denote any order, quantity or importance, but are only used to distinguish different components. Words such as "including" or "comprising" mean that the elements or objects appearing before this word cover the elements or objects listed after this word and their equivalents, without excluding other elements or objects. Words such as "connected" or "coupled" are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Upper", "lower", "left", "right", etc. are only used to represent relative position relationships, and when the absolute position of the object being described changes, the relative position relationship may also change accordingly.

[0019] Embodiment 1:

[0020] Please refer to Figure 1 , the present invention provides a technical solution:

[0021] A sound quality tuning method combining a vacuum tube and a TFT screen display, applied to a digital audio device, and the digital audio device is connected to a wearable device with a physiological parameter monitoring function, characterized by comprising:

[0022] Step S1: Obtain the spectrum optimization information of the input audio signal by the vacuum tube module at the current moment to obtain an initial sound quality parameter combination;

[0023] Further explanation, the objects of the spectrum optimization information include spectrum distribution parameters and signal gain parameters; the specific operation steps are as follows:

[0024] Receive an external audio signal through the input interface of the digital audio device;

[0025] Start the vacuum tube module to perform preliminary processing on the received input audio signal;

[0026] Extract the spectrum distribution parameters from the vacuum tube module, that is, the energy distribution of the audio signal in different frequency bands;

[0027] Extract the signal gain parameters from the vacuum tube module, that is, the amplification factor or attenuation degree of the signals in each frequency band;

[0028] Form the extracted spectrum distribution parameters and signal gain parameters into spectrum optimization information;

[0029] The vacuum tube module includes a working temperature dynamic monitoring unit, and the working temperature dynamic monitoring unit is used to monitor the temperature change of the vacuum tube in real time and trigger corresponding hierarchical warnings. The optimization strategies for the following spectrum distribution parameters and signal gain parameters are triggered by different levels of warnings:

[0030] In this embodiment, the real-time monitoring of the temperature change of the vacuum tube is achieved by collecting the temperature data of the vacuum tube during operation and recording the temperature change curve;

[0031] Set at least two temperature warning values for different levels of temperature warning. Specifically:

[0032] When the temperature of the vacuum tube is in the approaching interval of the two temperature warning values, start the first-level warning;

[0033] The first-level warning includes:

[0034] Start the spectrum distribution parameter optimization module and perform softening processing on the high-frequency band: adjust the spectrum distribution parameters of the high-frequency band, reduce the gain of the high-frequency signal; reduce the harshness;

[0035] When the temperature of the vacuum tube is above the maximum temperature warning value, start the second-level warning;

[0036] The second-level warning includes:

[0037] Start the signal gain parameter optimization module and perform amplitude adjustment on the signal gain parameters of each frequency band: reduce the amplitude of the signal gain parameters; in the high-power drive frequency band, reduce the thermal load on the vacuum tube;

[0038] Represent the initially optimized spectrum distribution parameters and signal gain parameters as an initial sound quality parameter combination;

[0039] Receive the initially optimized spectrum distribution parameters and signal gain parameters;

[0040] Through the digital signal processing module, perform initial optimization processing on the input audio signal for corresponding high-frequency softening or signal gain adjustment.

[0041] It should be noted that the specific implementation of different levels of temperature warning is as follows:

[0042] Start the working temperature dynamic monitoring unit and start real-time monitoring of the working temperature of the vacuum tube module;

[0043] Collect the temperature data of the vacuum tube during operation at a frequency of once per second;

[0044] Record the temperature change curve and store the temperature data in the internal memory of the device;

[0045] Perform real-time analysis on the collected temperature data and calculate the difference between the current temperature and multiple preset temperature warning values;

[0046] According to the monitoring results of the working temperature dynamic monitoring unit, the implementation steps for triggering the corresponding level of hierarchical warning are:

[0047] Set the following temperature warning values:

[0048] First-level warning value: 60°C; Second-level warning value: 75°C;

[0049] Define the approaching interval for the first-level warning as 60°C ≤ vacuum tube temperature < 75°C;

[0050] Define the range of the second-level warning as the vacuum tube temperature ≥ 75°C;

[0051] When the vacuum tube temperature monitored in real time enters the approaching interval of the first-level warning 60°C ≤ vacuum tube temperature < 75°C, start the first-level warning;

[0052] When the vacuum tube temperature monitored in real time is above 75°C, start the second-level warning;

[0053] When the first-level warning is triggered, perform softening processing on the high-frequency band through the spectrum distribution parameter optimization module. The specific operation steps are as follows:

[0054] Receive the first-level warning signal and confirm that the vacuum tube temperature enters the approaching interval of the first-level warning 60°C ≤ vacuum tube temperature < 75°C;

[0055] Start the spectrum distribution parameter optimization module and perform softening processing on the high-frequency band. Specifically, reduce the spectrum distribution parameter of the high-frequency band above 2 kHz by 3 dB;

[0056] Adjust the filter gain of the high-frequency band to reduce the output intensity of the high-frequency signal;

[0057] Feed back the optimized spectrum distribution parameter to the digital signal processing module and apply it to the real-time processing of audio signals;

[0058] Execute the signal gain parameter optimization strategy under the second-level warning. When the second-level warning is triggered, adjust the amplitude of the signal gain parameters of each frequency band through the signal gain parameter optimization module;

[0059] The specific operation steps are as follows:

[0060] Receive the second-level warning signal and confirm that the vacuum tube temperature is above 75°C;

[0061] Characterize each frequency band as a low-frequency band, a middle-frequency band, and a high-frequency band;

[0062] Low-frequency band: 20 Hz - 250 Hz, Middle-frequency band: 250 Hz - 2 kHz, High-frequency band: 2 kHz - 20 kHz;

[0063] Start the signal gain parameter optimization module and adjust the amplitude of the signal gain parameters of each frequency band. Specifically, reduce the signal gain parameters of all frequency bands by 5%;

[0064] Set the high-power drive frequency band in the high-frequency band to 4 kHz - 6 kHz, and the secondary warning signal is used to reduce the signal gain parameter by 10% in the high-power drive frequency band;

[0065] It should be noted that: To verify the effectiveness of further reducing the signal gain parameter by 10% in the high-power drive frequency band of 4 kHz - 6 kHz in reducing the power load, this experiment designed a multi-stage test scheme including a control group and an experimental group. The purpose of the experiment is to explore the specific effects of adjusting the gain in this frequency band on the vacuum tube temperature, power consumption, audio signal quality, and equipment stability.

[0066] 1. Equipment selection and setup:

[0067] Two digital audio devices of the same model were used in the experiment, marked as Sample A (control group) and Sample B (experimental group) respectively. Both devices are equipped with a vacuum tube module, and the experiment was carried out under the same environmental conditions. The ambient temperature was maintained at room temperature of 22 °C, and the devices were continuously operated to simulate a long-time audio playback load.

[0068] Each device is equipped with a high-precision temperature sensor to monitor the working temperature of the vacuum tube in real time. In addition, a power consumption recording device was installed to ensure accurate measurement of the power consumption under different gain settings.

[0069] 2. Signal generation and processing:

[0070] An audio signal generator was used to generate a swept-frequency signal covering the full frequency band (20 Hz to 20 kHz) to test the response characteristics of the device. The control group (Sample A) did not make any gain adjustment, while the experimental group (Sample B) reduced the signal gain by 10% in the high-power drive frequency band of 4 kHz - 6 kHz. This adjustment was achieved through a digital signal processing unit to ensure the accuracy of the gain adjustment.

[0071] 3. Experimental steps:

[0072] Initial test: Start Sample A and Sample B, play the same frequency-swept audio signal, and record the vacuum tube temperature, power consumption, and audio spectrum distribution in the initial state.

[0073] Gain adjustment: Reduce the signal gain parameter by 10% for Sample B in the high-power drive frequency band of 4 kHz - 6 kHz. Sample A remains unchanged with the original gain setting.

[0074] Operation and data acquisition: During the experiment, the audio signal was continuously played, and the vacuum tube temperature, power consumption, and spectrum analysis results of the device were recorded every 10 minutes until the experiment was completed.

[0075] Sound quality analysis: After the experiment, an audio analysis software was used to evaluate the sound quality output by the two devices, especially the sound quality changes in the high-frequency band, including harshness and sound quality balance.

[0076] 4. Experimental variables:

[0077] The experimental variables included the gain setting (Sample A was the original setting, and Sample B had a 10% reduction in gain), as well as changes in the operating temperature and power consumption of the vacuum tube. The data was collected at a frequency of once every 10 minutes, and the experiment lasted for 60 minutes.

[0078] 1. Temperature and power comparison:

[0079] In the control group sample A, the temperature of the vacuum tube gradually increased during the experiment and finally reached 77°C, and the power consumption gradually increased from 100W to 130W, showing the characteristic of high power consumption when the gain was not adjusted.

[0080] In the experimental group sample B under the same experimental conditions, due to a 10% reduction in signal gain in the 4kHz - 6kHz frequency band, the temperature increase of its vacuum tube was slower, and the final temperature only rose to 68°C. The power consumption also remained at a low level, increasing from 100W to 88W, reducing the significant power burden.

[0081] 2. Sound quality analysis:

[0082] Through comparative analysis, in the experimental group sample B, due to the reduction of the gain in the high-frequency band, the harshness of the sound quality was significantly reduced. The user listening test showed that when playing the same audio signal, the high-frequency sound of sample B became softer and more balanced, avoiding excessive harsh high-frequency interference, thus improving the overall auditory experience.

[0083] 3. Power load and device stability:

[0084] By adjusting the gain in the high-power drive frequency band, sample B significantly reduced the power load of the device, reduced the risk of overheating of the vacuum tube, improved the stability of the device, and avoided the impact of long-term high-temperature operation on the life of the vacuum tube.

[0085] The following is the data collected during the experiment, showing the comparison of temperature changes, power consumption, audio spectrum, and gain parameters of sample A and sample B at different time points.

[0086] Table 1 Research on reducing the signal gain parameter by 10% in the high-power drive frequency band of 4kHz - 6kHz:

[0087]

[0088] Table 1 Data analysis and demonstration:

[0089] 1. Temperature and power comparison:

[0090] The vacuum tube temperature of Sample A increased from 50°C to 77°C, and the power consumption increased from 100W to 130W, indicating that both the temperature and power consumption were relatively high when the device was not adjusted for gain.

[0091] The vacuum tube temperature of Sample B increased from 50°C to 68°C, and the power consumption increased from 100W to 88W, showing a significant effect after the gain reduction. The increase in temperature and power consumption was much lower than that of Sample A.

[0092] 2. Sound quality evaluation:

[0093] In the spectral analysis, the frequencies in the high-frequency band of Sample A (especially 4kHz - 6kHz) showed a strong sharpness. By reducing the gain in this frequency band for Sample B, the audio output became more balanced and gentle, greatly improving the sound quality performance in the high-frequency part and reducing auditory fatigue.

[0094] 3. Device stability:

[0095] By reducing the gain in the 4kHz - 6kHz frequency band, Sample B significantly reduced the power load and the thermal load on the vacuum tube, thus improving the device stability and avoiding performance degradation caused by overheating. Compared with Sample A, the temperature control of Sample B was more ideal during the experiment, and the device was more durable and stable.

[0096] This experiment verified the effectiveness of reducing the signal gain by 10% in the high-power driving frequency band of 4kHz - 6kHz in reducing the power load. By reducing the gain in this frequency band, the power consumption of the device and the temperature rise of the vacuum tube can be significantly reduced, thereby improving the device stability and extending its service life. At the same time, the optimization of the sound quality reduced the harshness in the high-frequency band, making the sound quality more balanced and soft. The above experimental data fully supported the practicality of further reducing the signal gain parameter by 10% in the high-power driving frequency band of 4kHz - 6kHz in reducing the power load, demonstrating its advantages in practical applications.

[0097] Feed the optimized signal gain parameter back to the digital signal processing module for real-time processing of audio signals;

[0098] Perform high-frequency softening or signal gain adjustment processing on the input audio signal through the digital signal processing module; specific operation steps:

[0099] Receive the optimized spectral distribution parameter and signal gain parameter.

[0100] According to the optimized parameters, perform the following processing through the digital signal processing module:

[0101] Apply a low-pass filter to the high-frequency signal to ensure that the signal above 2kHz is attenuated by 3dB;

[0102] According to the signal gain parameter, adjust the gain value of each frequency band; it should be noted that the high-power drive frequency band 4kHz-6kHz is the area that needs attention in the high frequency band;

[0103] Most existing technologies are single-module sound quality adjustment systems. This solution integrates the vacuum tube module with the TFT screen to achieve real-time display of optimization parameters, enhancing user experience and ease of operation.

[0104] Dynamic temperature monitoring and graded warning: The temperature of the vacuum tube can be monitored in real time through the working temperature dynamic monitoring unit, and corresponding optimization strategies can be triggered according to different temperature levels to improve the intelligence and safety of the equipment and avoid performance degradation or damage of the equipment due to overheating;

[0105] Quantitative optimization strategy: Specific temperature warning values and adjustment ranges are set to make the sound quality adjustment strategy operational and feasible.

[0106] Step S2: responding to the spectrum optimization information, and using the TFT screen to display the initial sound quality parameter combination after spectrum optimization in real time, and displaying the distortion evaluated after adjustment based on the initial sound quality parameter combination;

[0107] Further explanation: the TFT screen is a touch screen, and the digital signal processing module outputs the audio signal that has undergone initial optimization processing to the TFT screen display unit, so as to realize the real-time sound quality adjustment effect display, and the sound quality adjustment effect display includes displaying the sound quality parameter combination and distortion after the spectrum optimization;

[0108] The specific implementation contents are as follows:

[0109] The spectrum optimization information from the signal gain parameter optimization module is received through the digital signal processing module; the information includes the gain value adjustment instructions of each frequency band and the related sound quality parameters.

[0110] Parse the received spectrum optimization information to extract the specific gain value, filter type and parameter settings of each frequency band to ensure the data accuracy of subsequent processing steps;

[0111] Generate an initial sound quality parameter combination according to the parsed spectrum optimization information; the initial sound quality parameter combination includes the spectrum distribution parameter and signal gain parameter after initial optimization;

[0112] Configure filter settings: Determine and configure the filter type (such as low-pass, high-pass, band-pass filter) and its parameters (such as cutoff frequency, filter order) corresponding to each frequency band to achieve the desired spectrum optimization effect.

[0113] Integrate audio quality parameters: Integrate the generated spectral distribution parameters and signal gain parameters to form a complete initial audio quality parameter combination, ensuring that the audio signal processing module can accurately apply these parameters for audio quality optimization.

[0114] Activate the distortion evaluation module to calculate the distortion of the generated initial audio quality parameter combination; adopt the calculation method of total harmonic distortion (THD) to quantify the distortion of the audio signal under the optimized parameters; and record the distortion as THD.

[0115] Total harmonic distortion calculation formula:

[0116]

[0117] Explanation of formula parameters: V1 is the fundamental voltage, referring to the voltage amplitude of the fundamental frequency component in the audio signal;

[0118] V2, V3, V4, …, V n are the harmonic voltages of each order, corresponding to the voltage amplitudes of the second, third, fourth, etc. harmonic components of the fundamental frequency respectively;

[0119] n is the highest order of harmonics, depending on the complexity of the audio signal and the processing ability of the device;

[0120] Calculation process of THD(%):

[0121] Measure the fundamental voltage V1 and each harmonic voltage V2, V3, V4, …, V n .

[0122] Add the squares of each harmonic voltage and calculate the square root to obtain the total harmonic voltage.

[0123] Divide the total harmonic voltage by the fundamental voltage and multiply by 100 to obtain the total harmonic distortion percentage.

[0124] Record the calculated THD value as a reference index for subsequent optimization.

[0125] Perform the following configurations for the TFT touch screen display unit:

[0126] Set the communication interface between the TFT touch screen and the digital signal processing module to ensure that the audio quality parameters and distortion information can be transmitted to the display unit in real time.

[0127] Design the user interface layout on the TFT touch screen, including:

[0128] Audio quality parameter display area: Display the gain values of each frequency band, filter type and its parameter settings in the initial audio quality parameter combination;

[0129] Distortion display area: Display the calculated distortion percentage (THD value);

[0130] Interactive control area: Provide touch operation buttons or sliders to allow users to adjust sound quality parameters.

[0131] The digital signal processing module transmits the generated initial sound quality parameter combinations to the TFT touch screen display unit to display the gain values and filter settings of each frequency band in real time.

[0132] Display the distortion evaluation result: Display the calculated distortion (THD value) in the distortion display area for users to refer to;

[0133] Ensure that the changes in sound quality parameters are synchronized and updated with the calculation results of the distortion, and provide real-time feedback.

[0134] In the above operation steps, the reason for choosing to use the TFT touch screen to display the initial sound quality parameter combinations and distortion after spectrum optimization in real time is that the TFT touch screen has high resolution and sensitive touch response capabilities, and can intuitively display complex sound quality parameters and real-time distortion information.

[0135] Step S3: Collect the real-time physiological parameters of the user end under different sound quality parameter combinations and distortion, and analyze these data through a deep learning model to generate a sound quality tuning strategy adapted to the current initial sound quality parameter combination and distortion based on the real-time physiological parameters, and obtain the adjusted first-level sound quality parameter combination index;

[0136] Further explanation: The connection method between the digital audio device and the wearable device with physiological parameter monitoring function is as follows:

[0137] Pair and connect the digital audio device and the wearable device through Bluetooth or other wireless communication methods;

[0138] Collect the real-time physiological parameters of the user end from the wearable device, including heart rate and skin conductance response.

[0139] Transmit the collected real-time physiological parameter data to the digital audio device for subsequent deep learning analysis;

[0140] The real-time physiological parameters include detecting the user's heart rate and skin electrical signals through the wearable device;

[0141] The acquisition of the sound quality tuning strategy includes:

[0142] Input the collected real-time physiological parameters together with the current initial sound quality parameter combination and distortion data into the deep learning model;

[0143] The deep learning model deeply analyzes the collected user real-time physiological parameter data, the current sound quality parameter combination and audio distortion data by loading the pre-trained sound quality parameter combination sample library and distortion optimization sample library;

[0144] The deep learning model extracts features from the input data and matches the dynamic change characteristics of the combination of real-time physiological parameters and current sound quality parameters;

[0145] The deep learning model performs correlation identification on the mapping relationship between the user's real-time physiological parameters and the combination of current sound quality parameters, and finally generates a sound quality tuning strategy for adjusting the current initial sound quality parameter combination and optimizing the distortion degree;

[0146] Apply the generated sound quality tuning strategy to the digital signal processing module to achieve the primary optimization of the initial sound quality parameter combination and obtain the primary sound quality parameter combination;

[0147] The specific implementation content of this embodiment is as follows:

[0148] Form a real-time physiological parameter data set from the collected heart rate and galvanic skin response data to ensure the integrity and accuracy of the data;

[0149] Pair each real-time physiological parameter data set with the current initial sound quality parameter combination and its corresponding distortion degree to form a data pair;

[0150] Ensure the time-synchronized recording of the real-time physiological parameter data set with the sound quality parameter combination and the distortion degree for the subsequent accurate analysis of the deep learning model.

[0151] Transmit the paired real-time physiological parameter data set to the digital audio device through wireless communication to ensure low latency and high reliability of data transmission.

[0152] Establish a data storage and caching mechanism inside the digital audio device to temporarily store the received real-time physiological parameter data for preparing for deep learning analysis.

[0153] Represent the initial sound quality parameter combination as the initial sound quality parameter combination index G;

[0154] The steps for obtaining the initial sound quality parameter combination index G are as follows:

[0155] Multiply the normalized spectrum parameters of each frequency band by the normalized signal gain parameters to obtain the comprehensive sound quality contribution of the frequency band.

[0156] Take the average value of the comprehensive sound quality contributions of all frequency bands to obtain the current initial sound quality parameter combination index G; the calculation formula is as follows:

[0157]

[0158] Among them, G is the current initial sound quality parameter combination index, and the effective range is 0 < G ≤ 1; N is the number of frequency bands; F i is the spectrum distribution parameter of the i-th frequency band; F maxis the maximum value of the spectral distribution parameters for all frequency bands and is used for normalization; A i is the signal gain parameter for the i-th frequency band; A max is the maximum value of the signal gain parameters for all frequency bands and is used for normalization;

[0159] Normalize the spectral distribution parameter F for each frequency band i by dividing it by the maximum value F of this parameter max to ensure that the normalization result is within the range;

[0160] Normalize the signal gain parameter A for each frequency band i by dividing it by the maximum value A of this parameter max to ensure that the normalization result is within the range;

[0161] The spectral distribution parameter F i : represents the spectral energy distribution of each frequency band. The higher the value, the stronger the energy of that frequency band;

[0162] The signal gain parameter A i : represents the signal gain level of each frequency band. The higher the value, the greater the gain of that frequency band;

[0163] The initial sound quality parameter combination index G: is used to comprehensively evaluate the overall sound quality performance of the current initial sound quality parameter combination;

[0164] When G approaches 1 more closely, it indicates that the optimization state of the current initial sound quality parameter combination in all frequency bands is better;

[0165] When G approaches 0 more closely, it indicates that there is more room for optimization of the current initial sound quality parameter combination in multiple frequency bands;

[0166] The deep learning model loads the pre-trained sound quality parameter combination sample library and the distortion optimization sample library to ensure that the model has sufficient learning ability and matching ability;

[0167] Input data preparation: Input the collected real-time physiological parameter data HR, skin conductance response SE, the current initial sound quality parameter combination index G and its corresponding distortion data THD into the deep learning model together to form the input layer data structure of the model;

[0168] The deep learning model extracts features from the input data to comprehensively represent the relationship between the real-time physiological parameters and the current initial sound quality parameter combination; The specific formula is as follows:

[0169] P = σ(w1·HR norm +w2·SE norm +w3·G + w4·THD norm );

[0170] Among them, P is the comprehensive physiological parameter score, with the range 0 < P < 1; σ is the normalization function, defined as:

[0171]

[0172] w1, w2, w3, and w4 are the weight coefficients of the corresponding parameters, and the values of w1, w2, w3, and w4 are all within the interval (0, 1), and w1 + w2 + w3 + w4 = 1;

[0173] w1 is the heart rate weight coefficient, indicating the influence degree of heart rate on the comprehensive score; w2 is the skin conductance response weight coefficient, indicating the influence degree of skin conductance response on the comprehensive score; w3 is the voice quality parameter combination weight coefficient, indicating the influence degree of the voice quality parameter combination index on the comprehensive score; w4 is the distortion degree weight coefficient, indicating the influence degree of distortion degree on the comprehensive score;

[0174] HR norm is the normalized value of the real-time heart rate data, and the calculation formula is:

[0175]

[0176] Among them, the value range of the heart rate HR is HR min ≤ HR ≤ HR max , and after normalization, 0 ≤ HR norm ≤ 1. min and max are the index marks of the lower limit value and the upper limit value respectively;

[0177] SE norm is the normalized value of the real-time skin conductance response data, and the calculation formula is:

[0178]

[0179] Among them, the value range of the skin conductance response SE is SE min ≤ SE ≤ SE max , and after normalization, 0 ≤ SE norm ≤ 1.

[0180] G is the current initial voice quality parameter combination index;

[0181] THD norm is the normalized value of the distortion degree, and the calculation formula is:

[0182]

[0183] Among them, the value range of the distortion degree THD is determined according to the device performance, and after normalization, 0 ≤ THD norm ≤ 1.

[0184] Heart rate HR: The heart rate affects the user's mood and physical state. A high heart rate indicates excitement or stress, while a low heart rate indicates relaxation or fatigue;

[0185] Skin conductance response SE: The skin conductance response reflects the user's emotional changes and physiological stress state. A high skin conductance response indicates anxiety or excitement, while a low skin conductance response indicates calmness or relaxation.

[0186] Initial sound quality parameter combination index G: Comprehensively evaluate the overall sound quality performance of the current initial sound quality parameter combination. The higher the value, the more ideal the sound quality;

[0187] Total harmonic distortion THD: Using the calculation method of total harmonic distortion (THD), quantify the distortion of the audio signal under the optimized parameters. The lower the value, the smaller the sound quality distortion;

[0188] Comprehensive physiological parameter score P: Used to measure the matching degree between the current initial sound quality parameter combination and the user's physiological state.

[0189] When P approaches 1, it indicates that the matching degree between the current initial sound quality parameter combination and the user's physiological state is higher, and the sound quality optimization effect is more ideal;

[0190] When P approaches 0, it indicates that the matching degree between the current initial sound quality parameter combination and the user's physiological state is lower;

[0191] Weight coefficients w1, w2, w3, w4 are determined through experiments according to the pre-trained model to reflect the actual impact of each parameter on the comprehensive score;

[0192] Use the comprehensive physiological parameter score P to perform correlation identification on the mapping relationship between real-time physiological parameters and the current initial sound quality parameter combination. The formula for the sound quality tuning strategy score S is:

[0193]

[0194] Among them, S is the sound quality tuning strategy score, with a range of 0 < S < 1; β is the tuning sensitivity coefficient, reflecting the sensitivity of the adjustment process;

[0195] α is the tuning strategy adjustment amplitude coefficient, where 0 < α ≤ 1; control the adjustment strength of the tuning strategy through the tuning amplitude coefficient α to ensure that the tuning amplitude is within a reasonable range;

[0196] reflects the relative intensity of the comprehensive physiological parameter score P;

[0197] The setting of the logarithmic function can alleviate the influence of extreme values and ensure the smooth growth of the value of S;

[0198] γ is the weight coefficient of the dynamic correlation feature of the input layer parameters, indicating the influence degree of real-time parameters on the sound quality tuning strategy;

[0199] f(G, THD norm , HR norm , SE norm ) is a mapping function for dynamically extracting features from the original data of the input layer;

[0200] The mapping function f(G, THD norm , HR norm , SE norm ) is used to quantify the dynamic correlation relationship between the parameters of the input layer; the selected function form is based on weighted linear combination or exponential mapping, and is specifically defined as:

[0201] f(G, THD norm , HR norm , SE norm ) = w5·|G - THD norm | + w6·HR norm ·SE norm ;

[0202] w5 reflects the influence weight of the dynamic difference between the current initial sound quality parameter combination index G and the distortion degree THD norm on the sound quality adjustment;

[0203] w6 reflects the influence weight of the interaction between the real-time heart rate HR norm and the skin conductance response SE norm on the sound quality adjustment; w5 + w6 = 1;

[0204] |G - THD norm | is used to calculate the absolute difference between the initial sound quality parameter combination index G and the distortion degree THD norm , indicating the influence of the dynamic change between these two parameters on the sound quality optimization; capturing the influence of the difference between the initial sound quality parameter combination index G and the distortion degree on the optimization strategy;

[0205] HR norm ·SE norm is the interaction feature of the heart rate and the skin conductance response, reflecting the contribution of the user's physiological state to the sound quality optimization.

[0206] When S approaches 1 more, it indicates that the adjustment amplitude of the initial sound quality parameter combination index G needs to be greater;

[0207] When S approaches 0 more, it indicates that the adjustment amplitude of the initial sound quality parameter combination index G needs to be smaller;

[0208] If the difference between G and THD norm increases, f(G, THD norm , HR norm , SE norm)The eigenvalue of [] increases, resulting in an increase in S, indicating that the distortion needs to be improved;

[0209] If HR norm ·SE norm Increases, indicating that the user's emotional changes are large, and the sound quality adjustment needs to better meet the requirements of this state.

[0210] The sound quality adjustment strategy includes:

[0211] Apply the generated sound quality adjustment strategy score S to the digital signal processing module, and adjust the initial sound quality parameter combination index G and the distortion THD according to the following formula;

[0212] G′ = G + S·ΔG;

[0213] THD′ = THD - S·ΔTHD;

[0214] Among them, G′ is the adjusted primary sound quality parameter combination index, and the range is 0 < G′ ≤ 1;

[0215] THD′ is the adjusted distortion, and the range is determined according to the device performance. In this embodiment, 0 ≤ THD′ ≤ THD max ;

[0216] ΔG is the preset adjustment range of the initial sound quality parameter combination index G, and the range of ΔG is 0.3 ≤ G ≤ 8.1; it represents the maximum increase in adjusting the initial sound quality parameter combination index each time, ensuring the smoothness of the adjustment process.

[0217] ΔTHD is the preset distortion optimization range, and the range of ΔTHD is 0.32 ≤ ΔTHD ≤ 0.65; it represents the maximum range of optimizing the distortion each time, ensuring the effectiveness of the optimization process;

[0218] Adjust the current initial sound quality parameter combination index G according to the sound quality adjustment strategy score S to generate the adjusted primary sound quality parameter combination index G′;

[0219] Optimize the current distortion THD according to the sound quality adjustment strategy score S to generate the optimized distortion THD′;

[0220] When G′ increases, it indicates that the overall sound quality performance of the initial sound quality parameter combination has been improved;

[0221] When THD′ decreases, it indicates that the sound quality distortion degree decreases and the sound quality is purer;

[0222] According to the primary sound quality parameter combination index G′, obtain the adjusted spectral distribution parameter F i ′ and the signal gain parameter A i ′.

[0223] For the spectral distribution parameter F i ' Explanation:

[0224]

[0225] Among them, F i ' is the adjusted spectral distribution parameter in the first-level sound quality parameter combination index; F i is the spectral distribution parameter of the initial sound quality parameter combination index; G' is the first-level sound quality parameter combination index; G max is the maximum reference value of G' for normalization;

[0226] For signal gain parameter adjustment:

[0227]

[0228] Among them, A i ' is the adjusted signal gain parameter in the first-level sound quality parameter combination index; A i is the signal gain parameter of the initial sound quality parameter combination index; G ideal is the target index value required for ideal sound quality performance;

[0229] The adjusted first-level sound quality parameter combination should enter the next round of dynamic adjustment mechanism according to the user's physiological feedback.

[0230] The beneficial effects of the above embodiments are as follows:

[0231] By dynamically matching and correlating multi-dimensional physiological parameters such as heart rate and skin conductance response with the sound quality parameter combination index and distortion, it is ensured that the sound quality adjustment is not only based on technical parameters, but also more in line with the user's immediate physiological feedback, improving the user's auditory experience and the intelligent application level of the device; in addition, the designed mathematical formula realizes the quantitative relationship between physiological parameters and the sound quality parameter combination through comprehensive physiological parameter scoring and correlation calculation formulas, and realizes the dynamic optimization of sound quality through comprehensive scoring and sound quality tuning strategy scores, ensuring the effectiveness and rational design of the formula in practical technical applications.

[0232] Step S4: Respond to the sound quality tuning strategy and display the interactive tuning interface and the adjusted first-level sound quality parameter combination index through the TFT screen;

[0233] Further explanation: Displaying the interactive tuning interface and the adjusted first-level sound quality parameter combination index through the TFT screen, the specific logic includes:

[0234] Send the adjusted first-level sound quality parameter combination index to the TFT screen display unit;

[0235] Present the adjusted spectral distribution parameter F on the TFT screen in real time i' and signal gain parameter A i ';

[0236] The interactive tuning interface includes the display layout of the interactive display interface, including the spectral distribution parameter F i ' and signal gain parameter A i ' distribution diagram and specific values, so that users can intuitively understand the adjusted sound quality parameter information. Specifically include:

[0237] Spectral distribution curve graph: Through the drawing algorithm, generate the linear trend graph of the spectral distribution parameter F i ' of each frequency band;

[0238] Signal gain bar graph: Plot the signal gain parameter A i ' of each frequency band as a bar graph for display;

[0239] Present the spectral distribution curve graph in the center of the interface;

[0240] Display the signal gain bar graph at the bottom of the interface.

[0241] Set the TFT screen to the dynamic interaction mode, allowing users to interact with the interface content through touch or other operation methods, including but not limited to: selecting a specific frequency band to view the frequency band details.

[0242] Step S5: Receive the operation information adjusted in real time by the user through the TFT screen interactive tuning interface, and finally adjust the primary sound quality parameter combination index based on the operation information to generate the finally optimized secondary sound quality parameter combination index;

[0243] Further explanation: The interactive tuning interface also includes multiple preset sound quality configuration files. Users select one of the preset sound quality configuration files through the TFT screen interactive tuning interface, and the system receives and confirms the identification information of the selected preset sound quality configuration file; specific operations:

[0244] Multiple preset sound quality configuration files are displayed on the TFT screen. The sound quality configuration files include but are not limited to "movie mode", "music mode", "game mode";

[0245] Users select one of the preset sound quality configuration files on the TFT screen through touch operation;

[0246] The control module receives the user's selection instruction and identifies the identification information of the selected preset sound quality configuration file;

[0247] The control module confirms the validity of the selected configuration file and prepares to apply the corresponding sound quality tuning strategy;

[0248] Receive the identifier of the preset sound quality configuration file selected by the user. The control module calls the corresponding sound quality tuning strategy parameters and applies them to the primary sound quality parameter combination index to generate an intermediate adjusted secondary sound quality parameter combination index. Specifically:

[0249] The control module retrieves the corresponding pre-stored sound quality tuning strategy parameter set according to the identifier information of the preset sound quality configuration file. The sound quality tuning strategy parameter set includes spectral distribution parameters and signal gain parameters.

[0250] The control module applies the retrieved sound quality tuning strategy parameters to the current primary sound quality parameter combination index to adjust the corresponding spectral distribution parameters and signal gain parameters.

[0251] Based on the adjusted primary sound quality parameter combination index, calculate and generate an intermediate secondary sound quality parameter combination index G″ for subsequent optimization.

[0252] Further, on the basis of applying the preset sound quality configuration file, the user fine-tunes the primary sound quality parameter combination index through the TFT screen interactive tuning interface to obtain fine-tuning operation information. Specifically, it includes:

[0253] Receive and parse the user's fine-tuning operation information to further refine the adjustment of the secondary sound quality parameter combination index or the primary sound quality parameter combination index.

[0254] The TFT screen displays sound quality parameter micro-control components, such as sliders or knobs, for refining the adjustment of the TFT screen display sound quality parameter micro-control components of each frequency band, including sliders or knobs, for refining the adjustment of the spectral distribution parameters and signal gain parameters of each frequency band.

[0255] The user increases or decreases the spectral distribution parameters and signal gain parameters of a specific frequency band through touch operations.

[0256] The control module receives the user's fine-tuning operation instruction and parses out the specific adjustment amplitude and target parameters.

[0257] The control module verifies whether the received fine-tuning instruction is within the allowed adjustment range to ensure the effectiveness and safety of parameter adjustment.

[0258] Receive the user's fine-tuning operation information. The control module adjusts the primary sound quality parameter combination index, finally generates an optimized secondary sound quality parameter combination index, and updates the sound quality tuning state of the system.

[0259] Step S6: Use the secondary sound quality parameter combination index to adjust the audio signal gain and spectral distribution of the digital audio device, and display the adjustment result of the sound quality parameter combination on the TFT screen.

[0260] Further explanation: Receive and store the generated final optimized secondary audio quality parameter combination index G″ as the basic data for subsequent adjustment of audio signal gain parameters and spectral distribution parameters; specific steps:

[0261] The control module transmits the final optimized secondary audio quality parameter combination index to the audio quality adjustment module through the internal communication interface;

[0262] Data reception and storage: The audio quality adjustment module receives the transmitted secondary audio quality parameter combination index and stores it in the internal memory, preparing for subsequent audio quality adjustment operations;

[0263] Verify data integrity: The audio quality adjustment module performs integrity verification on the received secondary audio quality parameter combination index to ensure that the data is not lost or damaged during transmission.

[0264] Based on the received secondary audio quality parameter combination index, adjust the audio signal gain parameters of the digital audio device to achieve the preset audio quality optimization effect; specific steps:

[0265] Signal gain parameter extraction: Extract the signal gain adjustment values corresponding to each frequency band from the stored secondary audio quality parameter combination index;

[0266] Signal gain parameter calculation: According to the extracted signal gain adjustment values, calculate the specific gain adjustment amount for each frequency band. The formula is as follows:

[0267]

[0268] Among them, A i ″ is the signal gain parameter in the secondary audio quality parameter combination index, A i ′ is the signal gain parameter in the primary audio quality parameter combination index, and k is the adjustment coefficient used to control the gain adjustment amplitude.

[0269] Signal gain application: Apply the calculated A i ″ to the gain control module of the audio decoder to adjust the signal gain parameters of each frequency band to optimize the audio quality performance.

[0270] Signal gain parameter adjustment confirmation: The audio quality adjustment module confirms that the signal gain parameters have been successfully applied and records the adjusted signal gain parameter values for subsequent use.

[0271] Based on the received secondary audio quality parameter combination index, adjust the spectral distribution parameters of the digital audio device to achieve the preset audio quality optimization effect; specific steps:

[0272] Spectral distribution parameter extraction: Extract the spectral distribution adjustment values corresponding to each frequency band from the stored secondary audio quality parameter combination index;

[0273] Spectrum distribution parameter calculation: Based on the extracted spectrum distribution parameters, calculate the specific spectrum distribution adjustment amount for each frequency band. The formula is as follows:

[0274]

[0275] Where F i ′ is the spectrum distribution parameter in the first-level sound quality parameter combination index, F i ″ is the spectrum distribution parameter in the second-level sound quality parameter combination index, and m is the adjustment coefficient used to control the spectrum distribution adjustment amplitude;

[0276] Apply the calculated F i ″ to the spectrum adjustment function of the audio signal processing module to adjust the spectrum distribution parameters of each frequency band to optimize the sound quality performance;

[0277] The sound quality adjustment module confirms that the spectrum distribution parameters have been successfully applied and records the adjusted spectrum distribution parameter values for subsequent use.

[0278] Based on the adjusted audio signal gain parameters and spectrum distribution parameters, generate adjustment result data and send it to the TFT screen display module through the communication interface to present the adjusted sound quality parameter combination result on the display interface. Specific steps:

[0279] Integrate the adjusted signal gain parameter A i ″ and the spectrum distribution parameter F i ″ to form a complete adjustment result data set.

[0280] Perform encoding processing on the adjustment result data set and convert it into a format suitable for the TFT screen display module, such as JSON or binary data packet.

[0281] Send the encoded adjustment result data packet to the TFT screen display module through the internal communication interface.

[0282] The sound quality adjustment module waits for and receives the confirmation signal from the TFT screen display module to ensure that the adjustment result data has been successfully transmitted.

[0283] The TFT screen display module receives the sent adjustment result data and uses the display driver to display the adjusted signal gain parameter A i ″ and the spectrum distribution parameter F i ″ in a graphical and numerical form on the TFT screen in real time for the user to view. Specific steps:

[0284] The TFT screen display module receives the adjustment result data packet from the sound quality adjustment module and decodes and extracts A i ″ and F i ″ through the data parsing module.

[0285] Interface update preparation: According to the parsed parameters, prepare the specific content for updating the display interface, including the numerical display of signal gain and the graphical display of spectral distribution.

[0286] Graphical display generation: Through the graphics processing algorithm, generate i ″ a spectral distribution curve graph and i ″ a signal gain bar graph.

[0287] Update the TFT screen display interface to present the adjusted sound quality parameter combination results, including:

[0288] Display the finally optimized secondary sound quality parameter combination indicators;

[0289] Spectral distribution curve graph; Signal gain bar graph;

[0290] The TFT screen display module confirms that the page has been successfully updated and feeds back a completion signal to the sound quality adjustment module to end the operation process of step S6.

[0291] It should be noted that: All calculation formulas in this application document adopt regression analysis including but not limited to machine learning algorithms to deeply analyze the relevant parameters collected, identify their natural trends and interrelationships. Use professional software such as the Scikit-learn library of Python or the R language to automatically generate a mathematical model that matches the data. Then, objectively evaluate the model performance through methods such as cross-validation, and combine continuous feedback and optimization to ensure that the created formula truly reflects the internal laws of the data, thereby ensuring its effectiveness and accuracy. In all calculation formulas of this application, the parameters in each formula are processed by dimensionless normalization within a consistent range to ensure the comparison of different physical quantities on the same scale; The dimensionless technical means include but not limited to Min-Max Normalization and Z-Score standardization;

[0292] The technical solution of the present invention can be embodied in the form of a software product in essence or the part that contributes to the prior art. This computer software product can be stored in a computer-readable storage medium, such as a floppy disk, read-only memory (ROM), random access memory (RAM), flash memory (FLASH), hard disk or optical disc of a computer, etc., including several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute the methods of various embodiments of the present invention.

[0293] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a definable list of executable instructions for implementing logical functions, and can be embodied specifically in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device), or used in conjunction with these instruction execution systems, apparatuses, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0294] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered by the scope of the claims of the present invention.

[0295] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered by the scope of the claims of the present invention.

Claims

1. A sound quality tuning method combining a vacuum tube and a TFT screen display, characterized in that, The specific steps include: Obtaining spectrum optimization information of the vacuum tube module on the input audio signal at the current moment, and obtaining an initial sound quality parameter combination; Responding to the spectrum optimization information, the TFT screen is used to display the initial sound quality parameter combination after spectrum optimization in real time, and the distortion degree evaluated after adjustment based on the initial sound quality parameter combination is displayed at the same time; Collect the real-time physiological parameters of the user end under different sound quality parameter combinations and distortions, and analyze these data through a deep learning model to generate a sound quality adjustment strategy based on the real-time physiological parameters to adapt to the current initial sound quality parameter combination and distortion, and obtain the adjusted first-level sound quality parameter combination index; Respond to the sound quality adjustment strategy and display the interactive adjustment interface and the adjusted first-level sound quality parameter combination indicators through the TFT screen; Receive the operation information adjusted in real time by the user end through the TFT screen interactive adjustment interface, and make final adjustments to the primary sound quality parameter combination index based on the operation information to generate a final optimized secondary sound quality parameter combination index; The secondary sound quality parameter combination index is used to adjust the audio signal gain and spectrum distribution of the digital audio equipment, and the adjustment result of the sound quality parameter combination is displayed on the TFT screen.

2. A sound quality calibration method combining a vacuum tube and a TFT screen display according to claim 1, characterized in that: The objects of spectrum optimization information include spectrum distribution parameters and signal gain parameters; The vacuum tube module includes a working temperature dynamic monitoring unit, which is used to monitor the temperature change of the vacuum tube in real time and trigger corresponding graded warnings. Different levels of warnings trigger optimization strategies for the following spectrum distribution parameters and signal gain parameters: Set at least two temperature warning values for different levels of temperature warnings, specifically: When the temperature of the vacuum tube is within the approaching range of two temperature warning values, the first level warning is activated; Level 1 warnings include: Start the spectrum distribution parameter optimization module to perform softening processing on the high frequency band: adjust the spectrum distribution parameters of the high frequency band to reduce the gain of the high frequency signal; When the temperature of the vacuum tube is above the maximum temperature warning value, the second level warning is activated; The second level warning includes: Start the signal gain parameter optimization module to adjust the amplitude of the signal gain parameter of each frequency band: reduce the amplitude of the signal gain parameter; The initially optimized spectrum distribution parameters and signal gain parameters are expressed as an initial sound quality parameter combination; receiving initially optimized spectrum distribution parameters and signal gain parameters; The digital signal processing module performs initial optimization processing of high-frequency softening or signal gain adjustment on the input audio signal.

3. A sound quality calibration method combining a vacuum tube and a TFT screen display according to claim 2, characterized in that: Characterize each frequency band into a low frequency band, a mid frequency band, and a high frequency band; The high power driving frequency band in the high frequency band is set to 4kHz-6kHz, and the secondary warning signal is used to reduce the signal gain parameter by 10% in the high power driving frequency band.

4. A sound quality tuning method combining a vacuum tube and a TFT screen display according to claim 3, characterized in that: The TFT screen is a touch screen, and the digital signal processing module outputs the audio signal that has undergone initial optimization processing to the TFT screen display unit, so as to realize the real-time sound quality adjustment effect display, and the sound quality adjustment effect display includes displaying the sound quality parameter combination and distortion after the spectrum optimization; The total harmonic distortion calculation method is used to quantify the distortion of the audio signal under the optimized parameters; and the distortion is recorded as THD.

5. A sound quality tuning method combining a vacuum tube and a TFT screen display according to claim 4, characterized in that: The real-time physiological parameters include the heart rate and skin electrical signals of the user detected by the wearable device; The acquisition of the sound quality tuning strategy includes: Inputting the collected real-time physiological parameters, the current initial sound quality parameter combination, and the distortion data into the deep learning model together; The deep learning model deeply analyzes the collected real-time physiological parameter data of the user, the current sound quality parameter combination, and the audio distortion data by loading the pre-trained sound quality parameter combination sample library and the distortion optimization sample library; The deep learning model extracts features from the input data and matches the dynamic change characteristics of the real-time physiological parameters and the current sound quality parameter combination; The deep learning model conducts correlation recognition on the mapping relationship between the user's real-time physiological parameters and the current sound quality parameter combination, and finally generates a sound quality tuning strategy for adjusting the current initial sound quality parameter combination and optimizing the distortion; Applying the generated sound quality tuning strategy to the digital signal processing module to achieve the primary optimization of the initial sound quality parameter combination and obtain the primary sound quality parameter combination; Representing the initial sound quality parameter combination as the initial sound quality parameter combination index G; The steps for obtaining the initial sound quality parameter combination index G are as follows: Multiplying the standardized spectrum parameters of each frequency band by the standardized signal gain parameters to obtain the comprehensive sound quality contribution of the frequency band; Taking the average value of the comprehensive sound quality contributions of all frequency bands to obtain the current initial sound quality parameter combination index G; The deep learning model extracts features from the input data and comprehensively represents the relationship between the real-time physiological parameters and the current initial sound quality parameter combination; the specific formula is as follows: P = σ(w1·HR norm + w2·SE norm + w3·G + w4·THD norm ); Among them, P is the comprehensive physiological parameter score, where 0 < P < 1; σ is the normalization function, w1, w2, w3, and w4 are the weight coefficients of the corresponding parameters, and the values of w1, w2, w3, and w4 are all in the interval (0, 1), and w1 + w2 + w3 + w4 = 1; HR norm is the normalized value of the real-time heart rate data, SE norm is the normalized value of the real-time skin conductance response data, THD norm is the normalized value of the distortion degree; When P approaches 1 more, it indicates that the matching degree between the current initial sound quality parameter combination and the user's physiological state is higher, and the sound quality optimization effect is more ideal; When P approaches 0 more, it indicates that the matching degree between the current initial sound quality parameter combination and the user's physiological state is lower.

6. A sound quality tuning method combining a vacuum tube and a TFT screen display according to claim 5, characterized in that: Using the comprehensive physiological parameter score P to conduct correlation recognition on the mapping relationship between the real-time physiological parameters and the current initial sound quality parameter combination; Among them, S is the sound quality tuning strategy score, with a range of 0 < S < 1; β is the tuning sensitivity coefficient, reflecting the sensitivity of the adjustment process; α is the adjustment amplitude coefficient of the calibration strategy, where 0 < α ≤ 1; γ is the weight coefficient of the dynamically associated features of the input layer parameters, and f(G, THD norm , HR norm , SE norm ) is the mapping function for dynamically extracting features from the original data of the input layer; when S approaches 1 more closely, it indicates that the adjustment amplitude of the initial sound quality parameter combination index G is greater; When S approaches 0 more, it indicates that the adjustment amplitude required for the initial sound quality parameter combination index G is smaller; The sound quality tuning strategy includes: Applying the generated sound quality tuning strategy score S to the digital signal processing module and adjusting the initial sound quality parameter combination index G and the distortion THD according to the following formula; G′ = G + S·ΔG; THD′ = THD - S·ΔTHD; Among them, G′ is the adjusted primary sound quality parameter combination index, with a range of 0 < G′ ≤ 1; THD′ is the adjusted distortion, ΔG is the preset adjustment amplitude of the initial sound quality parameter combination index G, and ΔTHD is the preset distortion optimization amplitude. When G′ increases, it indicates that the overall sound quality performance of the initial sound quality parameter combination is improved; When THD′ decreases, it indicates that the degree of sound quality distortion is reduced and the sound quality is purer; According to the first-level sound quality parameter combination index G′, the adjusted spectral distribution parameter F i ′ and the signal gain parameter A′ i .

7. A sound quality tuning method combining a vacuum tube and a TFT screen display according to claim 6, characterized in that: Displaying the interactive tuning interface and the adjusted primary sound quality parameter combination index through the TFT screen, and the specific logic includes: Sending the adjusted primary sound quality parameter combination index to the TFT screen display unit; The adjusted spectral distribution parameter F i ′ and the signal gain parameter A i ′ are presented in real time on the TFT screen; The interactive calibration interface includes the display layout of the interactive display interface, including the distribution diagram and specific values of the spectrum distribution parameter F i ′ and the signal gain parameter A i ′.

8. A sound quality tuning method combining a vacuum tube and a TFT screen display according to claim 7, characterized in that: The interactive calibration interface further includes a plurality of preset sound quality profiles. The user selects one of the preset sound quality profiles through the TFT screen interactive calibration interface, and the system receives and confirms the identification information of the selected preset sound quality profile; Receiving the identification of the preset sound quality profile selected by the user, the control module calls the corresponding sound quality calibration strategy parameters and applies them to the first-level sound quality parameter combination index to generate an intermediate adjusted second-level sound quality parameter combination index; On the basis of applying the preset sound quality profile, the user fine-tunes the first-level sound quality parameter combination index through the TFT screen interactive calibration interface to obtain fine-tuning operation information; Receiving the user's fine-tuning operation information, the control module adjusts the first-level sound quality parameter combination index, finally generates an optimized second-level sound quality parameter combination index, and updates the sound quality calibration state of the system.

9. A sound quality calibration method combining a vacuum tube and a TFT screen display according to claim 8, characterized in that: Receiving and storing the finally optimized second-level sound quality parameter combination index G″ generated as the basic data for subsequent adjustment of the audio signal gain parameter and the spectrum distribution parameter; Adjust the audio signal gain parameter of the digital audio device based on the received secondary sound quality parameter combination index, and set the signal gain parameter in the secondary sound quality parameter combination index to A i ″; Based on the received second-level sound quality parameter combination index, adjusting the spectrum distribution parameter of the digital audio device; Set the spectral distribution parameter in the secondary sound quality parameter combination index to F i ″; Based on the adjusted audio signal gain parameter and spectrum distribution parameter, generating adjustment result data and sending it to the TFT screen display module through the communication interface to present the adjusted sound quality parameter combination result on the display interface; The TFT screen display module receives the adjusted result data sent, and through the display driver, the signal gain parameter A i ″ and the spectral distribution parameter F i ″ are displayed in a graphical and numerical form on the TFT screen in real time for the user to view.

Images