A data processing method for adaptive audio data collection and amplification

Abstract

The application discloses a kind of self-adapting audio data acquisition and data processing method of sound reinforcement, which can realize the high-quality output of audio, introduce intelligent equipment feedback module, the result of the modified parameter and data detection is fed back to audio data acquisition array and audio data preprocessing module by communication module, not only can self-adapting adjustment sound reinforcement system audio, can also adjust sound reinforcement system audio according to different standard artificial, solve the limitation of traditional sound reinforcement system only manual debugging, also solve the problem of unreliable, inflexible of improved self-adapting sound reinforcement system tone quality adjustment, introduce digital technology and huffman coding, solve the slow transmission speed of traditional sound reinforcement system, and the resource consumption problem in the process of storage and forwarding.A kind of self-adapting audio data acquisition and data processing method specifically includes: audio data acquisition array, audio data preprocessing module, audio data transmission module, loudspeaker output array and intelligent equipment feedback module.

Description

Technical Field

[0001] This invention relates to the field of sound processing and amplification, specifically a data processing method for adaptive audio data acquisition and amplification. Background Technology

[0002] Sound reinforcement systems are well-known audio equipment. However, their performance is affected by speaker placement and the surrounding environment, often resulting in slow transmission speeds, high storage media loss, timbre distortion, dry sound, and an inability to reproduce true-to-life sound effects. Traditional methods for adjusting the sound quality of sound reinforcement systems involve placing sound testing equipment at the desired locations and establishing a wireless connection with the main unit. The testing system is then activated, and the equipment being tested is turned on. The testing equipment collects sound signals and sends them back to the main unit. Staff then manually adjust the mixing console or audio equipment based on the sound signals from each location. This method still requires manual operation, is complex, and cannot guarantee accuracy and consistency.

[0003] In recent years, with the development of digital signal processing technology, some new sound quality adjustment methods have been introduced into sound reinforcement systems. The existing sound quality adjustment methods mainly utilize digital delay processing and adaptive filtering methods, but they have the following problems: First, they are highly complex and costly to implement; second, they have certain limitations, as different environments and music have different requirements, requiring repeated adjustments to parameters; and third, they are difficult to standardize, resulting in significant differences in the same sound effect on different devices. Summary of the Invention

[0004] To address the above problems, this invention proposes an adaptive audio data acquisition and sound reinforcement data processing method. This method can achieve high-quality audio output, not only adaptively adjusting the sound reinforcement system audio but also manually adjusting the sound reinforcement system audio according to different standards. This solves the limitation of traditional sound reinforcement systems that can only be manually adjusted, and also solves the problems of unreliable and inflexible sound quality adjustment in improved adaptive sound reinforcement systems. By introducing digital technology and Huffman coding, the invention solves the problems of slow transmission speed and resource consumption during storage and forwarding in traditional sound reinforcement systems.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a data processing method for adaptive audio data acquisition and amplification, comprising the following steps:

[0006] Step 1, Audio Data Acquisition Array: A high-speed, high-precision sampling scheme is adopted, and the matrix gain is adjusted according to the feedback signal and the adjustment signal to facilitate subsequent mathematical processing;

[0007] Step two, signal data preprocessing, is the most important module in the system. It is used to process the input digital audio signal and transmit it to the audio transmission module. In this module, differential, filtering, DSP processing, frequency domain adjustment, and time domain adjustment sound effect processing operations related to the digital audio signal are implemented.

[0008] Step 3, signal transmission module. By using digital signals instead of electrical signals, transistor amplifiers and capacitors are eliminated, reducing the dependence on hardware and increasing stability. This avoids the inaccuracies caused by errors in electronic components.

[0009] Step four: Speaker output array, apply the adjusted digital signal to different output modes and formats, and convert the digital signal into an analog signal if necessary;

[0010] Step 5: Smart device feedback. Through these feedback mechanisms, smart devices can interact with users and provide services more effectively.

[0011] As a further technical solution of the present invention, in step one, the audio data acquisition array specifically includes the following steps:

[0012] Step 1, Preprocessing: Before data acquisition, a site survey and assessment are required to determine the specifications for the sound reinforcement system. This ensures that the acquired data is suitable for determining the configuration performance and parameters, including the quantity and location of the main and auxiliary sound sources. Based on feedback from the intelligent device, 1kHz test signals are output channel by channel. The audio data acquisition array collects the sound signals, and the gain of the audio data preprocessing module is gradually increased until the loudness of each channel meets the preset template value. The preset template value needs to be preset within the device.

[0013] Step 2, collecting feedback signals: The application in the smart device will display the relevant parameter information of the crossover, equalizer, and delay on the mobile phone screen. The user can input relevant parameters by adjusting the crossover parameters or manually drawing the frequency response curve. After analysis, the input parameters will be transmitted to the audio data acquisition array.

[0014] Step 3, acquire signal source signal: Use professional equipment to measure the noise in the venue, and use this as a reference unit to automatically filter the ambient sound using the sound difference method. Determine the signal type, select the sensor, connect the signal source and the acquisition equipment, set the sampling parameters, start the acquisition equipment, record and process the data, and finally stop the acquisition.

[0015] Step 4, Sampling: The analog audio signal is sampled through a sampler. The sampling frequency of the audio signal depends on the frequency range and recovery time of the signal itself, as well as the system's data processing capability. In this invention, the sampling frequency is 45kHz, that is, the audio signal is sampled 45,000 times per second.

[0016] Step 5, Storage: Store the signal in computer memory, determine the storage medium for the sampled values ​​(device memory is used in this invention), determine the storage method for the sampled values, and select sequential storage based on data access requirements and performance needs.

[0017] As a further technical solution of the present invention, in step two, the signal data preprocessing specifically includes the following steps:

[0018] Step 1, Difference Operation: The discrete signal is obtained through sampling in Step 1. In the discrete time domain, difference can be achieved by calculating the difference between adjacent samples. The specific formula is as follows:

[0019] D[n] = a*(x[n] - x[n-1])

[0020] Wherein, the input signal is x[n], the differential signal is D[n], and a is the adjustment coefficient;

[0021] Step 2, Thresholding: Set a threshold and compare the absolute value of the differential signal with the threshold. When the differential value exceeds the set threshold, it can be considered that an abnormal fluctuation has occurred.

[0022] Step 3, Filtering: The fluctuating signal is filtered to eliminate noise and the effects of small fluctuations. The specific steps are as follows:

[0023] Step s1: Design a dual second-order IIR filter using an equalizer, and introduce dynamic volume compression and frequency response smoothing. The equalizer has five frequency bands with corresponding gains and center frequencies of G1, G2, G3, G4, G5 and f1, f2, f3, f4, f5, respectively. For each frequency band i, calculate the coefficients of the IIR filter using the following formula:

[0024]

[0025] Bi1 = -2*cos(wi);

[0026] Ai0 = -2*cos(wi);

[0027] Where Fs is the audio sampling rate, Fi is the center frequency of frequency band i, Bwi is the bandwidth of frequency band i, and Gi is the gain of frequency band i.

[0028] Step s2 involves controlling the dynamic range of the audio signal to prevent distortion of the output audio at high volumes. This process includes the following steps:

[0029] (1) When |x[n]|>T, Gv=1+(R-1)*(1-exp(-|x[n]|)); When |x[n]|<=T, Gv=1;

[0030] Where x[n] is the sample value of the input audio, y[n] is the sample value of the output audio, T is the compression threshold, R (R<1) is the compression coefficient, and Gv is the volume gain;

[0031] (2) Dynamic compression gain xc[n] = Gv*x[n]

[0032] (3) Introducing frequency response smoothing makes the transition between adjacent frequency bands smoother. The specific formula is as follows:

[0033]

[0034] Where Si is the smoothing coefficient between adjacent frequency bands i and i+1, P[n] is the output of frequency band i, and U+L[n] is the input of frequency band i+1;

[0035] (4) Smooth frequency response transition, the specific formula is as follows:

[0036] T[n]=Si*PL[n]+(1-Si)*U[n];

[0037] P+L[n]=P+L[n]+T[n];

[0038] Where f is the smoothed frequency range;

[0039] Step s3 involves inputting the audio signal T[n], after dynamic volume compression and frequency response smoothing, into an IIR filter for frequency adjustment. The specific process is as follows:

[0040] (1)Yi[n]=Bi0*T[n]+Bi1*T[n-1]+Bi2*T[n-2]-Ai1*Yi[n-1]-Ai2*Yi[n-2];

[0041] (2) Add the outputs of all frequency bands to obtain the final output: Y[n] = Y1[n] + Y2[n] + Y3[n] + Y4[n] + Y5[n];

[0042] Step s4: Adjust the dynamic range of the audio signal to make stronger signals weaker and increase the volume of weaker signals, thereby balancing and unifying the overall audio. The specific process is as follows:

[0043] (1) Calculate the gain of audio dynamic range control Dg = (1-Ds) + Ds*exp(-|T[n]|), where Ds is the intensity of audio dynamic range control;

[0044] (2) Audio dynamic range control gain: Xc[n] = Dg*T[n]

[0045] Step s5: Adjust the response of different frequency bands according to the dynamic changes of the audio signal to make the sound quality adjustment more adaptive. The specific process is as follows:

[0046] (1) Calculate the gain for dynamic response adjustment: Where DR_t is the threshold for dynamic response adjustment, and DR_g is the gain range;

[0047] (2) Apply dynamic response to adjust gain: Xr[n] = DR * Xc[n]

[0048] Step s6 involves additional smoothing to avoid discontinuities and sharp changes in sound quality. The specific process is as follows:

[0049] (1) Calculate the additional smoothing intensity es = 1 - exp(-es_s * (|Xr[n]| - |T[n]|)), where es_s is the additional smoothing intensity;

[0050] (2) Apply additional smoothing: Xf[n] = Xr[n] + es*(T[n] - Xr[n])

[0051] (3) Add the processed audio signals Xf[n] of all frequency bands together to obtain the final output:

[0052] y[n]=Xf1[n]+Xf2[n]+Xf3[n]+Xf4[n]+Xf5[n];

[0053] Step 4: Using a pre-emphasis network, the high-frequency components of the input signal are artificially emphasized, and then the opposite signal processing is performed at the output end, i.e., deemphasis. In the process of deemphasis, the high-frequency components of noise are reduced, which can improve the output signal-to-noise ratio.

[0054] Step 5: Based on different environments and audio types, a novel sound enhancement algorithm is used to analyze and process the audio signal. Combining the statistical characteristics of music signals and the human ear's perception mechanism, the audio signal is optimized without altering its dynamic range through frequency and time domain analysis. This specifically includes the following steps:

[0055] Step s1: Convert the audio signal from the time domain to the frequency domain using Fourier transform, and perform frequency analysis to obtain its energy and spectral distribution characteristics;

[0056] Step s2: By analyzing the energy distribution of the audio signal at different frequencies, the frequency range that needs to be processed is determined in order to enhance the dynamic range of the data.

[0057] Step s3: After obtaining the features through frequency domain analysis, the audio signal is analyzed in the time domain using wavelet analysis.

[0058] Step s4: By analyzing the instantaneous characteristics of the audio signal, determine the features and state of the audio signal at different time points, and further determine the signal parts that need to be enhanced;

[0059] Step s5 involves integrating the results of frequency and time domain analyses and using the Constant Distortion Awareness Model (CDA) algorithm to optimize the processing of the music signal.

[0060] As a further technical solution of the present invention, in step three, the signal transmission module specifically includes the following steps:

[0061] Step 1: The sampled signal needs to be quantized to convert the sampled value into a digital signal. The processed signal is then encoded into binary digital signals written in different types of codes. The higher the quantization precision, the better the sound quality of the digital signal. In this invention, 16-bit quantization precision is used to achieve high-quality audio output.

[0062] Step 2: Compress the sampled values ​​using Huffman coding to reduce the load on data storage and transmission. Huffman trees are a data compression algorithm that uses prefix coding to ensure that the code of any one character is not a prefix of the code of another character. The basic idea behind constructing a Huffman tree is to assign shorter codes to characters with higher frequency and longer codes to characters with lower frequency, thereby achieving efficient data compression. The specific steps are as follows:

[0063] Step s1: Count the frequency of each character in the data and construct character nodes based on the frequency;

[0064] Step s2: Construct a forest of character nodes (each node as a separate tree);

[0065] Step s3: Select the two trees with the smallest root node weights (weights represent the frequency of nodes) from the forest, merge them into a new tree, and set a virtual parent node for the new tree;

[0066] Step s4: The weight of the new tree is the sum of the weights of the two subtrees;

[0067] Step s5: Repeat the above steps until only one tree remains in the forest, namely the Huffman tree;

[0068] Step s6: By traversing the Huffman tree, the Huffman code of each character can be obtained. When traversing the left subtree of the tree starting from the root node, the code "0" is assigned, and when traversing the right subtree, the code "1" is assigned, until the leaf node is reached, and the Huffman code of the corresponding character can be obtained.

[0069] Step 3, Parameter Adjustment Storage: The adjusted parameters obtained from the algorithm processing are stored for quick retrieval next time. In this invention, to facilitate user adjustments to the algorithm and obtain optimal results, a parameter adjustment storage scheme is proposed. This scheme allows user-adjusted parameters to be saved locally and applied to the algorithm via loading. Specifically, it includes the following steps:

[0070] Step 1: When using the sound quality enhancement algorithm provided by this invention, users can adjust different parameters according to their needs and personal preferences, such as volume, sound quality, dynamic range, distortion perception, and intensity of various effects, in order to obtain the best effect.

[0071] Step 2: After the user adjusts the parameters, the adjusted parameters are saved on the local computer for easy use next time. The storage method used is local file. The next time it is used, the saved parameters are loaded and quickly applied to the algorithm for sound quality enhancement.

[0072] Step 3: When loading parameters, the present invention checks the validity of the parameters and corrects and updates them according to the actual situation to ensure the accuracy and usability of the parameters.

[0073] As a further technical solution of the present invention, step four specifically includes the following steps:

[0074] Step 1: Decode the compressed data: During decoding, the same Huffman tree is used to match the code bit by bit, and the decoding operation is performed according to the structure of the Huffman tree to finally restore the original data.

[0075] Step 2: Select different output modes according to needs and usage scenarios: output the processed signal directly to speakers or headphones for real-time playback; save the processed signal to a local file for later use; or transmit the processed signal to remote devices or users via the network to achieve remote control and audio resource sharing.

[0076] Step 3: While enhancing and processing the audio signal, the output format needs to be considered, including sampling rate, bit depth, and number of channels. When playing in real time, choose an output format with a high sampling rate, large bit depth, and many channels to obtain a clearer and higher fidelity sound quality. When saving to a local file, select different output formats based on the file format and storage capacity.

[0077] Step 4, consider output control: In real-time playback, the output volume, equalizer, and sound effects need to be controlled and adjusted to adapt to different music types and playback environments; when saving to local files, the output file name, path, format, and tags need to be set and controlled for easy file management and searching; and the sound source is processed through reverb effects to enhance the ambient atmosphere and make the listener feel natural and realistic.

[0078] Step 5: Optimize technologies and algorithms to improve the quality and effect of the output signal: Use dynamic halo technology and ambient sound enhancement technology to improve the quality and richness of the output signal. Reduce distortion by setting automatic audio cancellation in professional production software to make the audio effects richer during the production process.

[0079] As a further technical solution of the present invention, step five specifically includes the following steps:

[0080] Step 1: Modify the preset template values. Modifying the preset template values ​​allows the device to better adapt to user needs, enhances system flexibility and customizability, and provides a safer and more stable user experience. This includes the following methods:

[0081] s1, Using wireless communication: Establish a communication connection between the mobile phone and the device, and then use the corresponding application for remote control and configuration;

[0082] s2, Scan QR code: The device generates a QR code during initialization and attaches it to the device. The information on the QR code can be read by a scanning application on the mobile phone, and modification instructions can be sent to the device.

[0083] S3, Connect to the device's network interface: If the device provides a local area network interface, use a browser on your mobile phone to open the device's management page, and then make relevant settings and modifications;

[0084] Step 2, Adjusting parameters: The parameters are adjusted using a program. In this system, the smart device serves as the main parameter input device. The application in the smart device displays the crossover, equalizer, and delay parameters on the phone screen. Users can input the required parameters by adjusting the crossover parameters or manually drawing the frequency response curve. After analysis, the input parameters are transmitted to the audio data acquisition array and audio data preprocessing to perform corresponding operations on the audio signal.

[0085] Step 3: Data indicator detection. Use a voltmeter to detect the signal voltage, and use numerical judgment to test the ADC and DAC modules. Check whether the input level and output level test data are consistent. If they are not consistent, the mixing console settings need to be readjusted. Check whether the level data at the receiving end of the output audio signal module is close to 0. If not, pre-emphasis needs to be set, and the data needs to be fed back to the audio data acquisition array and audio data preprocessing.

[0086] Step 4, Intelligent Control Module: This module employs parallel processing, combined with feedback circuitry for data processing and transmission. When the hardware receives control commands and audio parameters from the intelligent device, it stores them in RAM and sends the required signal processing parameters and control commands to the audio data acquisition array and audio data preprocessing unit via the SPI interface. This method allows for simultaneous processing of intelligent control and audio processing tasks, improving overall processing efficiency. Attached Figure Description

[0087] The accompanying drawings are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without any creative effort.

[0088] Figure 1 This is a flowchart of an adaptive audio data acquisition and amplification data processing method according to the present invention. Detailed Implementation

[0089] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0090] An adaptive audio data acquisition and amplification data processing method includes the following steps:

[0091] Step 1, Audio Data Acquisition Array: A high-speed, high-precision sampling scheme is adopted, and the matrix gain is adjusted according to the feedback signal and the adjustment signal to facilitate subsequent mathematical processing;

[0092] Step two, signal data preprocessing, is the most important module in the system. It is used to process the input digital audio signal and transmit it to the audio transmission module. In this module, differential, filtering, DSP processing, frequency domain adjustment, and time domain adjustment sound effect processing operations related to the digital audio signal are implemented.

[0093] Step 3, signal transmission module. By using digital signals instead of electrical signals, transistor amplifiers and capacitors are eliminated, reducing the dependence on hardware and increasing stability. This avoids the inaccuracies caused by errors in electronic components.

[0094] Step four: Speaker output array, apply the adjusted digital signal to different output modes and formats, and convert the digital signal into an analog signal if necessary;

[0095] Step 5: Smart device feedback. Through these feedback mechanisms, smart devices can interact with users and provide services more effectively.

[0096] As a further technical solution of the present invention, in step one, the audio data acquisition array specifically includes the following steps:

[0097] Step 1, Preprocessing: Before data acquisition, a site survey and assessment are required to determine the specifications for the sound reinforcement system. This ensures that the acquired data is suitable for determining the configuration performance and parameters, including the quantity and location of the main and auxiliary sound sources. Based on feedback from the intelligent device, 1kHz test signals are output channel by channel. The audio data acquisition array collects the sound signals, and the gain of the audio data preprocessing module is gradually increased until the loudness of each channel meets the preset template value. The preset template value needs to be preset within the device.

[0098] Step 2, collecting feedback signals: The application in the smart device will display the relevant parameter information of the crossover, equalizer, and delay on the mobile phone screen. The user can input relevant parameters by adjusting the crossover parameters or manually drawing the frequency response curve. After analysis, the input parameters will be transmitted to the audio data acquisition array.

[0099] Step 3, acquire signal source signal: Use professional equipment to measure the noise in the venue, and use this as a reference unit to automatically filter the ambient sound using the sound difference method. Determine the signal type, select the sensor, connect the signal source and the acquisition equipment, set the sampling parameters, start the acquisition equipment, record and process the data, and finally stop the acquisition.

[0100] Step 4, Sampling: The analog audio signal is sampled through a sampler. The sampling frequency of the audio signal depends on the frequency range and recovery time of the signal itself, as well as the system's data processing capability. In this invention, the sampling frequency is 45kHz, that is, the audio signal is sampled 45,000 times per second.

[0101] Step 5, Storage: Store the signal in computer memory, determine the storage medium for the sampled values ​​(device memory is used in this invention), determine the storage method for the sampled values, and select sequential storage based on data access requirements and performance needs.

[0102] As a further technical solution of the present invention, in step two, the signal data preprocessing specifically includes the following steps:

[0103] Step 1, Difference Operation: The discrete signal is obtained through sampling in Step 1. In the discrete time domain, difference can be achieved by calculating the difference between adjacent samples. The specific formula is as follows:

[0104] D[n] = a*(x[n] - x[n-1])

[0105] Wherein, the input signal is x[n], the differential signal is D[n], and a is the adjustment coefficient;

[0106] Step 2, Thresholding: Set a threshold and compare the absolute value of the differential signal with the threshold. When the differential value exceeds the set threshold, it can be considered that an abnormal fluctuation has occurred.

[0107] Step 3, Filtering: The fluctuating signal is filtered to eliminate noise and the effects of small fluctuations. The specific steps are as follows:

[0108] Step s1: Design a dual second-order IIR filter using an equalizer, and introduce dynamic volume compression and frequency response smoothing. The equalizer has five frequency bands with corresponding gains and center frequencies of G1, G2, G3, G4, G5 and f1, f2, f3, f4, f5, respectively. For each frequency band i, calculate the coefficients of the IIR filter using the following formula:

[0109]

[0110] Bi1 = -2*cos(wi);

[0111] Ai0 = -2*cos(wi);

[0112] Where Fs is the audio sampling rate, Fi is the center frequency of frequency band i, Bwi is the bandwidth of frequency band i, and Gi is the gain of frequency band i.

[0113] Step s2 involves controlling the dynamic range of the audio signal to prevent distortion of the output audio at high volumes. This process includes the following steps:

[0114] (1) When |x[n]|>T, Gv=1+(R-1)*(1-exp(-|x[n]|)); When |x[n]|<=T, Gv=1;

[0115] Where x[n] is the sample value of the input audio, y[n] is the sample value of the output audio, T is the compression threshold, R (R<1) is the compression coefficient, and Gv is the volume gain;

[0116] (2) Dynamic compression gain xc[n] = Gv*x[n]

[0117] (3) Introducing frequency response smoothing makes the transition between adjacent frequency bands smoother. The specific formula is as follows:

[0118]

[0119] Where Si is the smoothing coefficient between adjacent frequency bands i and i+1, P[n] is the output of frequency band i, and U+L[n] is the input of frequency band i+1;

[0120] (4) Smooth frequency response transition, the specific formula is as follows:

[0121] T[n]=Si*PL[n]+(1-Si)*U[n];

[0122] P+L[n]=P+L[n]+T[n];

[0123] Where f is the smoothed frequency range;

[0124] Step s3 involves inputting the audio signal T[n], after dynamic volume compression and frequency response smoothing, into an IIR filter for frequency adjustment. The specific process is as follows:

[0125] (1)Yi[n]=Bi0*T[n]+Bi1*T[n-1]+Bi2*T[n-2]-Ai1*Yi[n-1]-Ai2*Yi[n-2];

[0126] (2) Add the outputs of all frequency bands to obtain the final output: Y[n] = Y1[n] + Y2[n] + Y3[n] + Y4[n] + Y5[n];

[0127] Step s4: Adjust the dynamic range of the audio signal to make stronger signals weaker and increase the volume of weaker signals, thereby balancing and unifying the overall audio. The specific process is as follows:

[0128] (1) Calculate the gain of audio dynamic range control Dg = (1-Ds) + Ds*exp(-|T[n]|), where Ds is the intensity of audio dynamic range control;

[0129] (2) Audio dynamic range control gain: Xc[n] = Dg*T[n]

[0130] Step s5: Adjust the response of different frequency bands according to the dynamic changes of the audio signal to make the sound quality adjustment more adaptive. The specific process is as follows:

[0131] (1) Calculate the gain for dynamic response adjustment: Where DR_t is the threshold for dynamic response adjustment, and DR_g is the gain range;

[0132] (2) Apply dynamic response to adjust gain: Xr[n] = DR * Xc[n]

[0133] Step s6 involves additional smoothing to avoid discontinuities and sharp changes in sound quality. The specific process is as follows:

[0134] (1) Calculate the additional smoothing intensity es = 1 - exp(-es_s * (|Xr[n]| - |T[n]|)), where es_s is the additional smoothing intensity;

[0135] (2) Apply additional smoothing: Xf[n] = Xr[n] + es*(T[n] - Xr[n])

[0136] (3) Add the processed audio signals Xf[n] of all frequency bands together to obtain the final output:

[0137] y[n]=Xf1[n]+Xf2[n]+Xf3[n]+Xf4[n]+Xf5[n];

[0138] Step 4: Using a pre-emphasis network, the high-frequency components of the input signal are artificially emphasized, and then the opposite signal processing is performed at the output end, i.e., deemphasis. In the process of deemphasis, the high-frequency components of noise are reduced, which can improve the output signal-to-noise ratio.

[0139] Step 5: Based on different environments and audio types, a novel sound enhancement algorithm is used to analyze and process the audio signal. Combining the statistical characteristics of music signals and the human ear's perception mechanism, the audio signal is optimized without altering its dynamic range through frequency and time domain analysis. This specifically includes the following steps:

[0140] Step s1: Convert the audio signal from the time domain to the frequency domain using Fourier transform, and perform frequency analysis to obtain its energy and spectral distribution characteristics;

[0141] Step s2: By analyzing the energy distribution of the audio signal at different frequencies, the frequency range that needs to be processed is determined in order to enhance the dynamic range of the data.

[0142] Step s3: After obtaining the features through frequency domain analysis, the audio signal is analyzed in the time domain using wavelet analysis.

[0143] Step s4: By analyzing the instantaneous characteristics of the audio signal, determine the features and state of the audio signal at different time points, and further determine the signal parts that need to be enhanced;

[0144] Step s5 involves integrating the results of frequency and time domain analyses and using the Constant Distortion Awareness Model (CDA) algorithm to optimize the processing of the music signal.

[0145] As a further technical solution of the present invention, in step three, the signal transmission module specifically includes the following steps:

[0146] Step 1: The sampled signal needs to be quantized to convert the sampled value into a digital signal. The processed signal is then encoded into binary digital signals written in different types of codes. The higher the quantization precision, the better the sound quality of the digital signal. In this invention, 16-bit quantization precision is used to achieve high-quality audio output.

[0147] Step 2: Compress the sampled values ​​using Huffman coding to reduce the load on data storage and transmission. Huffman trees are a data compression algorithm that uses prefix coding to ensure that the code of any one character is not a prefix of the code of another character. The basic idea behind constructing a Huffman tree is to assign shorter codes to characters with higher frequency and longer codes to characters with lower frequency, thereby achieving efficient data compression. The specific steps are as follows:

[0148] Step s1: Count the frequency of each character in the data and construct character nodes based on the frequency;

[0149] Step s2: Construct a forest of character nodes (each node as a separate tree);

[0150] Step s3: Select the two trees with the smallest root node weights (weights represent the frequency of nodes) from the forest, merge them into a new tree, and set a virtual parent node for the new tree;

[0151] Step s4: The weight of the new tree is the sum of the weights of the two subtrees;

[0152] Step s5: Repeat the above steps until only one tree remains in the forest, namely the Huffman tree;

[0153] Step s6: By traversing the Huffman tree, the Huffman code of each character can be obtained. When traversing the left subtree of the tree starting from the root node, the code "0" is assigned, and when traversing the right subtree, the code "1" is assigned, until the leaf node is reached, and the Huffman code of the corresponding character can be obtained.

[0154] Step 3, Parameter Adjustment Storage: The adjusted parameters obtained from the algorithm processing are stored for quick retrieval next time. In this invention, to facilitate user adjustments to the algorithm and obtain optimal results, a parameter adjustment storage scheme is proposed. This scheme allows user-adjusted parameters to be saved locally and applied to the algorithm via loading. Specifically, it includes the following steps:

[0155] Step 1: When using the sound quality enhancement algorithm provided by this invention, users can adjust different parameters according to their needs and personal preferences, such as volume, sound quality, dynamic range, distortion perception, and intensity of various effects, in order to obtain the best effect.

[0156] Step 2: After the user adjusts the parameters, the adjusted parameters are saved on the local computer for easy use next time. The storage method used is local file. The next time it is used, the saved parameters are loaded and quickly applied to the algorithm for sound quality enhancement.

[0157] Step 3: When loading parameters, the present invention checks the validity of the parameters and corrects and updates them according to the actual situation to ensure the accuracy and usability of the parameters.

[0158] As a further technical solution of the present invention, step four specifically includes the following steps:

[0159] Step 1: Decode the compressed data: During decoding, the same Huffman tree is used to match the code bit by bit, and the decoding operation is performed according to the structure of the Huffman tree to finally restore the original data.

[0160] Step 2: Select different output modes according to needs and usage scenarios: output the processed signal directly to speakers or headphones for real-time playback; save the processed signal to a local file for later use; or transmit the processed signal to remote devices or users via the network to achieve remote control and audio resource sharing.

[0161] Step 3: While enhancing and processing the audio signal, the output format needs to be considered, including sampling rate, bit depth, and number of channels. When playing in real time, choose an output format with a high sampling rate, large bit depth, and many channels to obtain a clearer and higher fidelity sound quality. When saving to a local file, select different output formats based on the file format and storage capacity.

[0162] Step 4, consider output control: In real-time playback, the output volume, equalizer, and sound effects need to be controlled and adjusted to adapt to different music types and playback environments; when saving to local files, the output file name, path, format, and tags need to be set and controlled for easy file management and searching; and the sound source is processed through reverb effects to enhance the ambient atmosphere and make the listener feel natural and realistic.

[0163] Step 5: Optimize technologies and algorithms to improve the quality and effect of the output signal: Use dynamic halo technology and ambient sound enhancement technology to improve the quality and richness of the output signal. Reduce distortion by setting automatic audio cancellation in professional production software to make the audio effects richer during the production process.

[0164] As a further technical solution of the present invention, step five specifically includes the following steps:

[0165] Step 1: Modify the preset template values. Modifying the preset template values ​​allows the device to better adapt to user needs, enhances system flexibility and customizability, and provides a safer and more stable user experience. This includes the following methods:

[0166] s1, Using wireless communication: Establish a communication connection between the mobile phone and the device, and then use the corresponding application for remote control and configuration;

[0167] s2, Scan QR code: The device generates a QR code during initialization and attaches it to the device. The information on the QR code can be read by a scanning application on the mobile phone, and modification instructions can be sent to the device.

[0168] S3, Connect to the device's network interface: If the device provides a local area network interface, use a browser on your mobile phone to open the device's management page, and then make relevant settings and modifications;

[0169] Step 2, Adjusting parameters: The parameters are adjusted using a program. In this system, the smart device serves as the main parameter input device. The application in the smart device displays the crossover, equalizer, and delay parameters on the phone screen. Users can input the required parameters by adjusting the crossover parameters or manually drawing the frequency response curve. After analysis, the input parameters are transmitted to the audio data acquisition array and audio data preprocessing to perform corresponding operations on the audio signal.

[0170] Step 3: Data indicator detection. Use a voltmeter to detect the signal voltage, and use numerical judgment to test the ADC and DAC modules. Check whether the input level and output level test data are consistent. If they are not consistent, the mixing console settings need to be readjusted. Check whether the level data at the receiving end of the output audio signal module is close to 0. If not, pre-emphasis needs to be set, and the data needs to be fed back to the audio data acquisition array and audio data preprocessing.

[0171] Step 4, Intelligent Control Module: This module employs parallel processing, combined with feedback circuitry for data processing and transmission. When the hardware receives control commands and audio parameters from the intelligent device, it stores them in RAM and sends the required signal processing parameters and control commands to the audio data acquisition array and audio data preprocessing unit via the SPI interface. This method allows for simultaneous processing of intelligent control and audio processing tasks, improving overall processing efficiency.

Claims

1. A data processing method for adaptive audio data acquisition and amplification, characterized in that, Includes the following steps: Step 1, Audio Data Acquisition Array: Captures sound to provide audio signals, and adjusts the matrix gain based on feedback and adjustment signals; Step two involves signal data preprocessing, using differential, filtering, frequency domain adjustment, and time domain adjustment to process the digital audio signal. The filtering section employs an equalizer design with dual second-order IIR filters to dynamically control audio, volume, and gain. This includes dynamic audio control, dynamic volume control, dynamic gain adjustment, and additional smoothing processing. The specific process is as follows: Step 1, Difference Operation: In the discrete time domain, difference is achieved by calculating the difference between adjacent samples. The specific formula is as follows: ； Wherein, the input signal is x[n], the differential signal is D[n], and a is the adjustment coefficient; Step 2, Threshold detection: Set a threshold and compare the absolute value of the differential signal with the threshold. When the differential value exceeds the set threshold, it is considered that there is an abnormal fluctuation, and the adjustment in Step 3 is performed. Step 3, Filtering: The fluctuating signal is filtered to eliminate noise and the effects of small fluctuations. Step 4: Using a pre-emphasis network, the high-frequency components of the input signal are artificially emphasized, and the opposite signal processing is performed at the output end to reduce the high-frequency components of noise, thereby improving the output signal-to-noise ratio. Step 5: Based on different environments and audio types, and combining the statistical characteristics of music signals with the human ear's perception mechanism, optimize the audio signal without altering its dynamic range through frequency and time domain analysis. Step 3: The signal transmission module uses Huffman coding to construct a variable-length code, reducing the number of bits required for data transmission and storage. The signal transmission module specifically includes the following steps: Step 1: Use 16-bit quantization precision to convert the sampled values ​​into digital signals, and encode the processed signals into binary digital signals written in different types of codes; Step 2: Compress the sampled values ​​using Huffman coding to reduce the load on data storage and transmission. The specific steps are as follows: Step s1: Count the frequency of each character in the data and construct character nodes based on the frequency; Step s2: Construct a forest of character nodes (each node as a separate tree); Step s3: Select the two trees with the smallest root node weights (weights represent the frequency of nodes) from the forest, merge them into a new tree, and set a virtual parent node for the new tree; Step s4: The weight of the new tree is the sum of the weights of the two subtrees; Step s5: Repeat the above steps until only one tree remains in the forest, namely the Huffman tree; Step s6: By traversing the Huffman tree, obtain the Huffman code of each character. When traversing the left subtree of the tree starting from the root node, assign the code "0" and when traversing the right subtree, assign the code "1" until the leaf node is reached, and the Huffman code of the corresponding character can be obtained. Step 3, Adjustment Parameter Storage: Store the adjustment parameters obtained from the algorithm processing for quick retrieval next time; Step four: Speaker output array, applying the adjusted digital signal to different output modes and formats; Step 5: Smart device feedback. Through the feedback mechanism, the smart device interacts with the user.

2. The adaptive audio data acquisition and amplification data processing method according to claim 1, characterized in that... In step one, the audio data acquisition array includes preprocessing, acquiring feedback signals, acquiring signal source signals, sampling, and storage. The specific process is as follows: Step 1, Preprocessing: Before data acquisition, determine the number and location of the main sound source and auxiliary sound sources; output 1kHz test signals channel by channel according to the feedback from the smart device; gradually increase the gain of the audio data preprocessing module through the sound signals acquired by the audio data acquisition array until the loudness of each channel meets the preset template value; Step 2, Acquire feedback signal: The application in the smart device displays the crossover, equalizer, and delay parameter information on the frequency screen. The parameters are input by adjusting the crossover parameters and manually drawing the frequency response curve. The input parameters are analyzed and then transmitted to the audio data acquisition array. Step 3, Acquire signal from the signal source: Use the acoustic difference method to automatically filter ambient sound, determine the signal type, select the sensor, connect the signal source and the acquisition device, set the sampling parameters, start the acquisition device, record and process the data, and finally stop the acquisition; Step 4, Sampling: The analog audio signal is sampled through a sampler at a sampling frequency of 45kHz. Step 5, Storage: Store the signal in computer memory, using the device memory storage medium. Select sequential storage method based on data access requirements and performance needs.

3. The adaptive audio data acquisition and amplification data processing method according to claim 1, characterized in that... In step two, the specific steps for using a filter to eliminate noise and the effects of minor fluctuations include: Step s1: Design a dual second-order IIR filter using an equalizer, and introduce dynamic volume compression and frequency response smoothing. The equalizer has five frequency bands with corresponding gains and center frequencies of G1, G2, G3, G4, G5 and f1, f2, f3, f4, f5, respectively. For each frequency band i, calculate the coefficients of the IIR filter using the following formula: ； ； ； ； ； ； ； ； ； Where Fs is the audio sampling rate, Fi is the center frequency of frequency band i, Bwi is the bandwidth of frequency band i, and Gi is the gain of frequency band i. Step s2: Control the dynamic range of the audio signal so that the output audio will not be distorted at high volumes; Step s3 involves inputting the audio signal T[n], after dynamic volume compression and frequency response smoothing, into an IIR filter for frequency adjustment. The specific process is as follows: （1） ； (2) Sum the outputs of all frequency bands to obtain the final output: ; Step s4: Adjust the dynamic range of the audio signal to make stronger signals weaker and increase the volume of weaker signals, thereby balancing and unifying the overall audio. The specific process is as follows: (1) Calculate the gain of audio dynamic range control Dg=(1-Ds)+Ds*exp(-|T[n]|), where Ds is the intensity of audio dynamic range control; (2) Audio dynamic range control gain: Xc[n] = Dg*T[n] Step s5: Adjust the response of different frequency bands according to the dynamic changes of the audio signal to make the sound quality adjustment more adaptive. The specific process is as follows: (1) Calculate the gain for dynamic response adjustment: Where DR_t is the threshold for dynamic response adjustment, and DR_g is the gain range; (2) Apply dynamic response to adjust gain: Xr[n] = DR * Xc[n] Step s6, additional smoothing process to avoid discontinuous and sharp changes in sound quality.

4. The adaptive audio data acquisition and amplification data processing method according to claim 3, characterized in that... In step s2, the specific steps for controlling the dynamic range of the audio signal include: (1) When |x[n]|>T, When |x[n]| <= T, Gv = 1; Where x[n] is the sample value of the input audio, y[n] is the sample value of the output audio, T is the compression threshold, R (R<1) is the compression coefficient, and Gv is the volume gain; (2) Dynamic compression gain xc[n] = Gv*x[n]; (3) Introducing frequency response smoothing makes the transition between adjacent frequency bands smoother. The specific formula is as follows: ； Where Si is the smoothing coefficient between adjacent frequency bands i and i+1, P[n] is the output of frequency band i, and U+L[n] is the input of frequency band i+1; (4) The formula for smooth frequency response is as follows: ; Where f is the smoothed frequency range.

5. The adaptive audio data acquisition and amplification data processing method according to claim 3, characterized in that... In step s6, the specific process of the additional smoothing is as follows: (1) Calculate the additional smoothing intensity es = 1 - exp(-es_s * (|Xr[n]| - |T[n]|)), where es_s is the additional smoothing intensity; (2) Apply additional smoothing: Xf[n] = Xr[n] + es*(T[n] - Xr[n]) (3) Add the processed audio signals Xf[n] of all frequency bands together to obtain the final output: y[n]=Xf1[n]+Xf2[n]+Xf3[n]+Xf4[n]+Xf5[n].

6. The adaptive audio data acquisition and amplification data processing method according to claim 1, characterized in that... In step two, the audio signal is optimized without changing its dynamic range through frequency and time domain analysis. This specifically includes the following steps: Step s1: Convert the audio signal from the time domain to the frequency domain using Fourier transform, perform frequency analysis on it, and obtain energy and spectral distribution characteristics; Step s2: By analyzing the energy distribution of the audio signal at different frequencies, the frequency range that needs to be processed is determined, thereby enhancing the dynamic range of the data. Step s3: After obtaining the features through frequency domain analysis, the audio signal is analyzed in the time domain using wavelet analysis. Step s4: By analyzing the instantaneous characteristics of the audio signal, determine the features and state of the audio signal at different time points, and determine the signal parts that need to be enhanced; Step s5: Use the constant distortion perception model algorithm to optimize the processing of the music signal.

7. The adaptive audio data acquisition and amplification data processing method according to claim 1, characterized in that... In step four, the speaker output array applies the adjusted digital signal to different output modes and formats, specifically including the following steps: Step 1: Decode the compressed data: During decoding, the same Huffman tree is used to match the code bit by bit, and the decoding operation is performed according to the structure of the Huffman tree to restore the original data. Step 2: Select different output modes according to needs and usage scenarios: output the processed signal directly to the speaker for real-time playback; or save the processed signal to a local file. The processed signal is transmitted to remote devices via the network to enable remote control and sharing of audio resources; Step 3: Enhance and process the audio signal. When playing it in real time, select an output format with a high sampling rate, large bit depth, and many channels. When saving it to a local file, select different output formats based on the file format and storage capacity. Step 4: Optimize technologies and algorithms to improve the quality and effect of the output signal: Use dynamic halo technology and ambient sound enhancement technology to improve the quality and richness of the output signal. Reduce distortion by setting automatic audio cancellation in professional production software to make the audio effects richer during the production process.

8. The adaptive audio data acquisition and amplification data processing method according to claim 1, characterized in that... In step five, the smart device provides feedback. Through these feedback mechanisms, the smart device interacts with the user, specifically including the following steps: Step 1: Modify the preset template values ​​to adapt the device to user needs and enhance the system's flexibility and customizability. This includes the following methods: s1, Using wireless communication: Establish a communication connection between the mobile phone and the device, and use the application for remote control and configuration; s2, Scan QR code: The device generates a QR code during initialization. The information on the QR code is read by the mobile phone, and the modification command is sent to the device. S3, Connect to the device's network interface: If the device provides a local area network interface, use a browser on your mobile phone to open the device's management page to make settings and modifications; Step 2, Adjust parameters: The application in the smart device displays the crossover, equalizer, and delay parameters on the phone screen. By adjusting the crossover parameters and manually drawing the frequency response curve, the required parameters are input and transmitted to the audio data acquisition array and audio data preprocessing to perform corresponding operations on the audio signal. Step 3: Data indicator detection. Use a voltmeter to detect the signal voltage, use numerical judgment to detect the ADC and DAC modules, and detect the input and output level test data; detect the level data at the receiving end of the output audio signal module, and feed the data back to the audio data acquisition array and audio data preprocessing. Step 4, Intelligent Control Module: Using parallel processing, when the hardware device receives control commands and audio parameters from the intelligent device, it stores them in RAM and sends the required signal processing parameters and control commands to the audio data acquisition array and audio data preprocessing through the SPI interface.

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