Wireless audio data transmission and reading method and audio playing equipment

Abstract

The invention relates to the technical field of audio processing, and discloses a wireless audio data transmission and reading method and audio playing equipment, and the method comprises the steps: carrying out the signal processing of an input original audio signal, obtaining digital audio data, and building a waveform equation; performing parameter optimization to obtain an optimal waveform equation parameter; creating a signal processor and processing to obtain an optimized audio signal; performing data division to obtain a plurality of audio data packets, and solving the transmission optimization model to obtain a transmission scheduling scheme; performing coding processing to obtain a coded audio stream, and performing multi-layer mapping to obtain a multi-layer OFDM (Orthogonal Frequency Division Multiplexing) symbol; the method comprises the following steps: receiving the audio data, performing signal reconstruction processing to obtain a receiving end signal, decoding the receiving end signal to obtain decoded audio data, and recombining the decoded audio data to obtain a reconstructed digital audio signal, thereby improving the wireless audio data transmission and reading performance.

Description

Technical Field

[0001] The present invention relates to the technical field of audio processing, and in particular to a wireless audio data transmission and reading method and an audio playing device. Background Art

[0002] Wireless audio data transmission and reading technology plays an increasingly important role in modern communications and multimedia applications. With the popularization of smart devices and the development of the Internet of Things, people have an increasing demand for high-quality, low-latency, stable and reliable wireless audio transmission. However, traditional wireless audio transmission methods face many challenges, such as signal distortion, transmission delay, data loss, etc., which seriously affect the user's auditory experience and communication quality.

[0003] In addition, existing wireless audio transmission technologies often perform poorly when dealing with complex network environments, especially when bandwidth is limited and channel conditions are poor, the audio quality will be significantly reduced. At the same time, existing technologies also have shortcomings in the optimization and processing of audio signals, and it is difficult to effectively remove noise, suppress harmonic distortion, and adapt to the characteristics of different types of audio content. Another urgent problem to be solved is the optimization of transmission scheduling. In a multi-user, multi-device scenario, how to reasonably allocate network resources to ensure the timely transmission of key audio data while taking into account the overall transmission efficiency is a complex challenge. Summary of the invention

[0004] The present invention provides a wireless audio data transmission and reading method and an audio playing device, which are used to improve the performance of wireless audio data transmission and reading.

[0005] In a first aspect, the present invention provides a method for wireless audio data transmission and reading, the method for wireless audio data transmission and reading comprising:

[0006] Performing signal processing on the input original audio signal to obtain digital audio data, and performing transformation processing on the digital audio data to obtain a frequency domain signal, and establishing a waveform equation based on the frequency domain signal;

[0007] Optimizing the waveform equation parameters to obtain optimized waveform equation parameters, constructing a fitness function based on the optimized waveform equation parameters, and optimizing the fitness function to obtain optimal waveform equation parameters;

[0008] Creating a signal processor based on the optimal waveform equation parameters, and processing the digital audio data by the signal processor to obtain an optimized audio signal;

[0009] Performing data division on the optimized audio signal to obtain a plurality of audio data packets, establishing a transmission optimization model based on the audio data packets, and solving the transmission optimization model to obtain a transmission scheduling solution;

[0010] Based on the transmission scheduling scheme, the multiple audio data packets are encoded to obtain an encoded audio stream, and the encoded audio stream is multi-layer mapped to obtain multi-layer OFDM symbols;

[0011] The multi-layer OFDM symbols are subjected to signal reconstruction processing to obtain a receiving end signal, the receiving end signal is decoded to obtain decoded audio data, and the decoded audio data is reorganized to obtain a reconstructed digital audio signal.

[0012] In a second aspect, the present invention provides an audio playback device, the audio playback device comprising:

[0013] A signal processing module, used to perform signal processing on the input original audio signal to obtain digital audio data, and perform transformation processing on the digital audio data to obtain a frequency domain signal, and establish a waveform equation based on the frequency domain signal;

[0014] A parameter optimization module, used to optimize the parameters of the waveform equation to obtain optimized waveform equation parameters, construct a fitness function based on the optimized waveform equation parameters, and optimize the fitness function to obtain optimal waveform equation parameters;

[0015] A creation module, used to create a signal processor based on the optimal waveform equation parameters, and process the digital audio data through the signal processor to obtain an optimized audio signal;

[0016] A solution module, configured to divide the optimized audio signal into data to obtain a plurality of audio data packets, establish a transmission optimization model based on the audio data packets, and solve the transmission optimization model to obtain a transmission scheduling solution;

[0017] A coding module, configured to encode the plurality of audio data packets based on the transmission scheduling scheme to obtain an encoded audio stream, and perform multi-layer mapping on the encoded audio stream to obtain multi-layer OFDM symbols;

[0018] The decoding module is used to perform signal reconstruction processing on the multi-layer OFDM symbols to obtain a receiving end signal, decode the receiving end signal to obtain decoded audio data, and reorganize the decoded audio data to obtain a reconstructed digital audio signal.

[0019] The third aspect of the present invention provides an electronic device, comprising: a memory and at least one processor, wherein the memory stores instructions; the at least one processor calls the instructions in the memory so that the electronic device executes the above-mentioned wireless audio data transmission and reading method.

[0020] A fourth aspect of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores instructions, which, when executed on a computer, enable the computer to execute the above-mentioned wireless audio data transmission and reading method.

[0021] In the technical solution provided by the present invention, by establishing an accurate waveform equation and optimizing parameters, the method can more accurately describe and process audio signals, effectively reduce signal distortion, and improve audio quality. A multi-level signal processor is used, including harmonic suppression, noise elimination, dynamic range adjustment, etc., which can comprehensively optimize audio signals and significantly improve sound quality. The introduction of a transmission optimization model and a compact transmission sequence algorithm realizes more efficient data packet transmission scheduling, reduces transmission delay, and improves network resource efficiency. The use of multi-layer OFDM symbol mapping technology enhances the adaptability and anti-interference ability of the system in a complex network environment and improves the reliability of transmission. Through layered coding and decoding technology, hierarchical transmission of audio data is realized, basic sound quality is guaranteed under limited bandwidth, and a higher quality audio experience is provided when conditions permit. The signal reconstruction and equalization processing technology at the receiving end effectively compensates for channel distortion during transmission and improves the restoration of the decoded audio. The overall solution has strong adaptability and scalability, can be adaptively adjusted according to different types of audio content and network conditions, and is suitable for a variety of application scenarios.

[0022] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description, claims and drawings.

[0023] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 A schematic diagram of an embodiment of a wireless audio data transmission and reading method in an embodiment of the present invention;

[0025] Figure 2 A schematic diagram of an embodiment of an audio playback device in an embodiment of the present invention;

[0026] Figure 3FIG. 1 is a schematic diagram of an electronic device in an embodiment of the present invention. DETAILED DESCRIPTION

[0027] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0028] The terms "including" and "having" and any variations thereof mentioned in the embodiments of the present invention are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device end including a series of steps or units is not limited to the listed steps or units, but may optionally include other steps or units that are not listed, or may optionally include other steps or units that are inherent to these processes, methods, products or device ends.

[0029] To facilitate understanding of this embodiment, a wireless audio data transmission and reading method disclosed in an embodiment of the present invention is first described in detail. Figure 1 As shown, the method comprises the following steps:

[0030] 101. Perform signal processing on the input original audio signal to obtain digital audio data, perform transformation processing on the digital audio data to obtain a frequency domain signal, and establish a waveform equation based on the frequency domain signal;

[0031] It is understandable that the execution subject of the present invention may be an audio playback device, or a terminal or a server, which is not limited here. The embodiment of the present invention is described by taking a server as the execution subject as an example.

[0032] Specifically, the input original audio signal is sampled. The discrete time signal is obtained by sampling the audio signal within a specific time interval. The discrete time signal is quantized, and each sample value of the discrete time signal is converted into a digital form to obtain digital audio data, and the amplitude of the sampling point is represented by a discrete value. The digital audio data is subjected to a fast Fourier transform, and the time domain signal is converted into a frequency domain signal to obtain the frequency component of the audio signal. The frequency domain signal is subjected to a spectrum analysis to obtain the spectrum characteristics of the signal, which reflects the amplitude distribution of each frequency component in the audio signal and their impact on the overall audio quality. By analyzing the spectrum characteristics, the possible sources of errors in the signal are identified. Error identification helps to find specific problems that may cause audio quality degradation, such as background noise, harmonic distortion or signal loss. Based on the identified target error source, the basic structure of the waveform equation is determined. The structure includes a fundamental frequency component, a harmonic component and a DC bias. The fundamental frequency component is the main frequency component of the audio signal, which usually represents the basic tone of the sound, while the harmonic component is an integer multiple of the fundamental frequency, and they together determine the sound quality and timbre of the audio signal. The DC bias reflects the average value of the signal and can affect the overall deviation of the signal. The fundamental frequency component is parameterized to obtain parameters representing the fundamental frequency amplitude, angular frequency and phase. The fundamental frequency amplitude reflects the strength of the signal's main frequency, the angular frequency determines the frequency of the signal, and the phase affects the relative position of the signal in time. The harmonic components of each order are parameterized to obtain parameters representing the amplitude and phase of each order of harmonics. Each harmonic has its own unique amplitude and phase. The DC bias is parameterized to obtain parameters representing the bias value. The size of the DC bias will affect the reference level of the signal, thereby affecting the actual playback effect of the audio. The parameters of the fundamental frequency, the harmonics and the DC bias are combined to form a waveform equation containing multiple parameters to be determined. The mathematical form of the waveform equation is:

[0033] Among them, y(t) represents the waveform equation, A is the amplitude of the fundamental frequency, ω is the angular frequency, φ is the phase, Bn and φn represent the amplitude and phase of each order of harmonics respectively, and C represents the DC bias value. In this way, an accurate mathematical model, namely the waveform equation, is constructed based on the input audio signal to describe the characteristics of the audio signal in the time domain and frequency domain.

[0034] 102. Optimize the parameters of the waveform equation to obtain optimized waveform equation parameters, construct a fitness function based on the optimized waveform equation parameters, and optimize the fitness function to obtain optimal waveform equation parameters;

[0035] Specifically, the undetermined parameters in the waveform equation are randomly initialized to obtain an initial parameter set. Based on the initial parameter set, the digital audio data is filtered to obtain an initial filtered signal. The characteristics of the audio signal are corrected and adjusted by the parameters of the waveform equation to make it as close to the optimized signal as possible. The initial filtered signal and the original digital audio data are differenced to obtain an initial residual signal, which represents the deviation between the filtered signal and the original audio signal. The initial residual square sum is obtained by calculating the square sum of the initial residual signal, which reflects the error degree of the filtered signal under the current parameters. The initial residual square sum is used as the initial value of the fitness function, and the initial fitness function is generated to measure the quality of the current parameter set. The smaller the value, the better the optimization effect of the signal. Based on the generated initial fitness function, the waveform equation parameters are gradient descent processed to calculate how to adjust each parameter in the parameter set to minimize the value of the fitness function. After each gradient descent update, an updated parameter set is obtained. Based on the updated parameter set, the coefficients of the adaptive filter are updated to obtain an updated adaptive filter. The updated adaptive filter is used to filter the digital audio data again to generate an updated filter signal. After obtaining the updated filter signal, the previous steps are repeated to calculate the difference between the updated filter signal and the digital audio data to obtain an updated residual signal, and the square sum is calculated to obtain an updated residual square sum. The updated residual square sum reflects the optimization effect after gradient adjustment. If the residual square sum value is less than the preset threshold, it means that the current parameter set is sufficiently optimized, the optimization process can be terminated, and the current parameter set is determined as the optimal waveform equation parameter. If the updated residual square sum is still greater than or equal to the preset threshold, it means that the optimization is not yet completed and it needs to continue to iterate. In this case, the system counts the number of iterations and compares the iteration count value with the preset maximum number of iterations. If the iteration count value reaches or exceeds the maximum number of iterations, it means that no further optimization can be performed within the specified number of times, the system will terminate the iteration, and the current parameter set will be output as the optimal waveform equation parameter. Through this cyclic iterative optimization process, the optimal waveform equation parameters are gradually approached to achieve the purpose of improving the audio transmission and reading quality.

[0036] 103. Create a signal processor based on the optimal waveform equation parameters, and process the digital audio data through the signal processor to obtain an optimized audio signal;

[0037] Specifically, based on the harmonic component information in the optimal waveform equation parameters, the harmonic suppression filter parameters are calculated. The main function of the harmonic suppression filter is to reduce or eliminate the harmonic distortion in the audio signal. The harmonic suppression filter is applied to filter the digital audio data to obtain the audio signal after harmonic suppression, thereby improving the clarity and accuracy of the audio. Based on the background noise characteristics in the optimal waveform equation parameters, the parameters of the adaptive noise eliminator are calculated. Through the adaptive noise eliminator, the performance of the filter is dynamically adjusted according to the noise characteristics, and the noise is effectively removed without damaging the audio signal. Based on the adaptive noise eliminator parameters, the audio signal after harmonic suppression is noise-eliminated to reduce the environmental noise or background interference in the audio signal, and obtain the audio signal after noise elimination. Based on the dynamic range information in the optimal waveform equation parameters, the parameters of the dynamic range adjuster are calculated to ensure that the different volume parts of the audio signal have appropriate loudness when playing, so as to avoid the problem of too loud or too small volume. Through the dynamic range adjuster, the lower volume part of the audio signal is boosted, while the peak of the higher volume part is controlled, ensuring that the audio signal has appropriate dynamic performance in the entire frequency range. Based on these parameters, the dynamic range of the audio signal after noise elimination is adjusted to obtain an audio signal with balanced dynamic characteristics. A phase compensation lookup table is generated based on the phase information in the optimal waveform equation. Phase distortion will affect the time accuracy and spatial perception of the audio signal. By performing phase correction on the audio signal after dynamic range adjustment based on the phase compensation lookup table, it is ensured that the various frequency components of the audio signal maintain correct time alignment and restore the original phase characteristics of the signal. The frequency response of the phase-corrected audio signal is analyzed to obtain the frequency response curve of the signal. The frequency response curve is an indicator of the energy distribution of the audio signal at different frequencies, which can reflect the frequency characteristics in the signal and which frequency bands need to be adjusted. Based on the frequency response curve, the corresponding equalizer parameters are calculated. The function of the equalizer is to adjust different frequency bands to achieve a balanced sound quality performance. Based on the equalizer parameters, the phase-corrected audio signal is equalized to ensure that the audio signal maintains a uniform energy distribution in each frequency range and obtain an optimized audio signal.

[0038] 104. Divide the optimized audio signal into data to obtain multiple audio data packets, establish a transmission optimization model based on the audio data packets, and solve the transmission optimization model to obtain a transmission scheduling solution;

[0039] Specifically, the optimized audio signal is divided into time windows, and the audio signal is divided into multiple audio data segments with fixed durations. Each audio data segment is subjected to frame synchronization processing to ensure the correctness and consistency of data transmission. Frame header information is added to each audio data segment to help the receiving end identify the starting position of each data packet, so that the transmission and reception process of the entire audio signal is more orderly, and multiple audio data packets are obtained. Priority is assigned to the generated audio data packets to obtain the priority value of each audio data packet. The allocation is performed according to multiple factors such as the content importance, time sensitivity, and network conditions of each data packet to ensure that key audio data packets are transmitted first during network transmission, thereby reducing the distortion and delay of the audio signal. Based on the priority value of the audio data packet and the network transmission conditions, a wireless audio transmission optimization model with time window constraints is constructed. The optimization model takes into account the real-time requirements of transmission, and ensures the stable transmission of audio signals in a wireless environment by combining network bandwidth, delay and other conditions. The wireless audio transmission optimization model is solved by applying a compact transmission sequence algorithm to obtain an initial transmission sequence. Based on the initial transmission sequence, the audio data packets are cyclically linearly exchanged to generate multiple different candidate transmission sequences. Cyclic linear exchange is an effective optimization strategy that finds potential better transmission solutions through different data packet arrangements. Candidate transmission sequences are evaluated, and the evaluation criteria usually include transmission delay, packet loss rate, bandwidth utilization, etc. An optimal transmission sequence is determined by evaluating the candidate transmission sequences. Based on the evaluated optimal transmission sequence, a corresponding transmission time window is allocated to each audio data packet, so that the audio data packet is transmitted at the most appropriate time point to minimize delay and packet loss problems and obtain a transmission scheduling solution.

[0040] The priority values ​​of audio data packets are normalized, and the priority values ​​of each audio data packet are converted into a unified scale to obtain a normalized priority value. The network transmission conditions are parameterized. Network transmission conditions include bandwidth, delay, packet loss rate and other aspects. Through parameterization, these conditions are quantified into specific network parameters. Based on the normalized priority value and network parameters, an objective function is constructed to minimize the weighted sum of the total transmission time and the packet loss rate, that is, while ensuring the lowest possible transmission time, the loss of data packets is minimized. The transmission time of the audio data packet is constrained to obtain a time window constraint. By setting the earliest and latest transmission time of the data packet, it is ensured that each data packet can be transmitted within the specified time range. The objective function and the time window constraint are combined to obtain a wireless audio transmission optimization model. The wireless audio transmission optimization model is converted into a linear programming model. Based on the linear programming model, a compact transmission sequence algorithm is constructed. The algorithm includes three main sub-processes: initialization, sequence generation and optimization. In the initialization stage, the relevant parameters are set. In the sequence generation subprocess, the audio data packets are sorted according to the earliest possible transmission time to obtain the initial transmission sequence. Based on the initial sequence, the sequence generation subprocess of the compact transmission sequence algorithm is applied to generate multiple candidate transmission sequences. The sequence generation process continuously arranges and combines the transmission order of data packets to find possible optimization solutions and form a set of candidate transmission sequences. The optimization subprocess of the compact transmission sequence algorithm is applied to the set of candidate transmission sequences to optimize the candidate sequences. The optimization subprocess selects the optimal transmission order through a weighted comprehensive evaluation of the transmission time and the packet loss rate, and adjusts it on this basis until the optimal balance between the transmission time and the packet loss rate is met. The initial transmission sequence is obtained.

[0041] 105. Based on the transmission scheduling scheme, encode multiple audio data packets to obtain encoded audio streams, and perform multi-layer mapping on the encoded audio streams to obtain multi-layer OFDM symbols;

[0042] Specifically, content analysis is performed on multiple audio data packets to obtain corresponding audio type information, which includes the type, compression rate, importance, etc. of the audio. Based on the audio type information, an appropriate coding algorithm is selected for each audio data packet. For example, for important audio parts, an efficient and reliable coding algorithm is selected, while for less important parts, an algorithm with a higher compression rate is selected. Through the selection, a coding algorithm set is constructed, which contains different coding schemes for different audio types. Multiple audio data packets are encoded based on the coding algorithm set, and the original audio data packets are converted into encoded audio streams. The encoded audio streams are layered to obtain basic layer and enhanced layer audio data. The basic layer audio data usually contains the core information of the audio, ensuring that the basic layer audio can be completely received and played even if data is lost during transmission. The enhanced layer audio data includes the high-quality part of the audio, which is used to provide better sound quality performance when the network condition is good. Based on the transmission scheduling scheme, the basic layer audio data is modulated and converted into basic layer OFDM symbols. OFDM symbols are a multi-carrier modulation technology that can effectively utilize bandwidth and reduce interference and signal loss during transmission, and are suitable for wireless transmission environments. The enhanced layer audio data is segmented and divided into multiple small segments for more flexible transmission scheduling and resource allocation. Based on the transmission scheduling scheme, multiple enhanced layer data segments are modulated to obtain multiple enhanced layer OFDM symbols. The enhanced layer OFDM symbols can be selectively transmitted according to network conditions during transmission, ensuring that the basic layer is transmitted first when the bandwidth is limited, and more enhanced layer symbols are transmitted when the bandwidth is sufficient to improve the sound quality. Power allocation is performed on the basic layer OFDM symbols and multiple enhanced layer OFDM symbols, and energy resources in wireless transmission are reasonably allocated to ensure that the basic layer audio symbols obtain sufficient power to ensure their reliable transmission, while the enhanced layer symbols can allocate the remaining power according to the transmission conditions. Through the allocation method, the overall efficiency of audio transmission can be improved and the bit error rate during transmission can be reduced. Based on the power allocation scheme, the basic layer OFDM symbols and multiple enhanced layer OFDM symbols are merged to obtain the final multi-layer OFDM symbols, which contain audio information at different levels.

[0043] 106. Perform signal reconstruction processing on the multi-layer OFDM symbols to obtain a receiving end signal, perform decoding processing on the receiving end signal to obtain decoded audio data, and reorganize the decoded audio data to obtain a reconstructed digital audio signal.

[0044] Specifically, the cyclic prefix of the received multi-layer OFDM symbol is removed to obtain a pure OFDM symbol sequence. The pure OFDM symbol sequence is transformed and converted from the time domain to the frequency domain to obtain a frequency domain signal, which contains the frequency information of the multi-layer audio data. Based on the preset power allocation scheme, the frequency domain signal is hierarchically demodulated to obtain a basic layer signal and multiple enhanced layer signals, ensuring that data of different priorities can be correctly processed during wireless transmission. Channel equalization is performed on the basic layer signal and multiple enhanced layer signals respectively, and the distortion of the signal is corrected by compensating for the interference and noise influence of the channel during transmission to obtain the equalized multi-layer signal. Based on the preset coding algorithm set, the equalized multi-layer signal is decoded. Since different layers of signals may use different coding algorithms during transmission, the signals of the basic layer and the enhanced layer are decoded according to the corresponding algorithms in the coding algorithm set during decoding. Through decoding, the original basic layer audio data and enhanced layer audio data are restored. The decoded basic layer audio data and enhanced layer audio data are merged to obtain complete decoded audio data. The decoded complete audio data is reassembled into packets, and the audio data packets divided during transmission are reassembled into a continuous digital audio data stream, restoring the time and content sequence of the original audio signal, ensuring that the data that was not lost or disordered during transmission can be rearranged into the correct format. The reassembled digital audio data stream is converted into digital-to-analog format, and the audio data in digital format is converted back into analog signals so that it can be played through devices such as speakers. Through digital-to-analog conversion, a reconstructed analog audio signal is finally obtained.

[0045] Perform power spectrum analysis on the frequency domain signal to obtain the power distribution of the signal in different frequency ranges and understand the energy distribution characteristics of the signal. Through the signal power distribution diagram, subcarrier allocation is performed on the frequency domain signal to obtain a basic layer subcarrier set and multiple enhancement layer subcarrier sets. The rationality of subcarrier allocation directly affects the data transmission efficiency and demodulation effect, so this process needs to comprehensively consider the signal priority and network conditions. Demodulate the basic layer subcarrier set to restore the basic layer signal. At the same time, demodulate the multiple enhancement layer subcarrier sets respectively to obtain the corresponding multiple enhancement layer signals. Based on the pre-estimated channel state information, channel estimation is performed on the basic layer signal to obtain the basic layer channel estimation result, which reflects the impact of the channel on the signal during the transmission process, such as channel attenuation and noise interference. Based on the basic layer channel estimation result, the basic layer signal is equalized, and the signal is restored to a state close to the transmitting end by compensating for the interference and distortion caused by the channel, and the equalized basic layer signal is obtained. At the same time, based on the pre-estimated channel state information and the basic layer channel estimation result, channel estimation is performed on each enhancement layer signal to obtain multiple enhancement layer channel estimation results, providing specific channel characteristic information. Based on the channel estimation results of multiple enhancement layers, channel equalization is performed on each enhancement layer signal to obtain multiple equalized enhancement layer signals, so that the correct transmission and demodulation of the enhancement layer data can be guaranteed even under poor channel conditions. The equalized basic layer signal and multiple equalized enhancement layer signals are combined to obtain an equalized multi-layer signal. The equalized multi-layer signal combines the stability of the basic layer and the high quality characteristics of the enhancement layer, and can provide basic audio output under unsatisfactory transmission conditions, while showing high-fidelity sound quality performance under good conditions.

[0046] In the embodiment of the present invention, by establishing an accurate waveform equation and optimizing parameters, the method can more accurately describe and process audio signals, effectively reduce signal distortion, and improve audio quality. The use of a multi-level signal processor, including harmonic suppression, noise elimination, dynamic range adjustment, etc., can comprehensively optimize the audio signal and significantly improve the sound quality. The introduction of a transmission optimization model and a compact transmission sequence algorithm realizes more efficient data packet transmission scheduling, reduces transmission delay, and improves network resource efficiency. The use of multi-layer OFDM symbol mapping technology enhances the adaptability and anti-interference ability of the system in a complex network environment and improves the reliability of transmission. Through layered coding and decoding technology, hierarchical transmission of audio data is realized, basic sound quality is guaranteed under limited bandwidth, and a higher quality audio experience is provided when conditions permit. The signal reconstruction and equalization processing technology at the receiving end effectively compensates for channel distortion during transmission and improves the restoration of the decoded audio. The overall solution has strong adaptability and scalability, can be adaptively adjusted according to different types of audio content and network conditions, and is suitable for a variety of application scenarios.

[0047] In a specific embodiment, the process of executing step 101 may specifically include the following steps:

[0048] The original audio signal is sampled to obtain a discrete time signal, and the discrete time signal is quantized to obtain digital audio data;

[0049] Perform fast Fourier transform on digital audio data to obtain frequency domain signal;

[0050] Perform spectrum analysis on the frequency domain signal to obtain the spectrum characteristics of the signal, and perform error identification based on the spectrum characteristics to obtain the target error source that affects the audio quality;

[0051] Determine the basic structure of the waveform equation based on the target error source, the basic structure including fundamental frequency component, harmonic component and DC bias;

[0052] The fundamental frequency component is parameterized to obtain parameters representing the fundamental frequency amplitude, angular frequency and phase; the harmonic component is parameterized to obtain parameters representing the amplitude and phase of each order of harmonics; the DC bias is parameterized to obtain parameters representing the bias value;

[0053] The parameters representing the fundamental frequency amplitude, angular frequency and phase, the parameters representing the amplitude and phase of each order harmonic, and the parameters representing the offset value are combined to obtain a waveform equation containing multiple parameters to be determined, where the waveform equation is:

[0054] y(t)=A*sin(ωt+φ)+Σ(Bn*sin(nωt+φn))+C;

[0055] Where y(t) represents the waveform equation, A is the fundamental frequency amplitude, ω is the angular frequency, φ is the phase, Bn and φn are the amplitude and phase of the harmonic components respectively, and C is the DC bias.

[0056] Specifically, the original audio signal is sampled to convert the continuous analog signal into a discrete time signal. Sampling converts the audio signal from the continuous domain to the discrete time domain by acquiring the instantaneous value of the audio signal at fixed time intervals. The sampling frequency is determined by the Nyquist sampling theorem, which states that the sampling frequency must be at least twice the highest frequency in the signal to avoid aliasing. For example, for a standard audio signal, if its highest frequency is 20kHz, the sampling frequency should be 40kHz or higher. The discrete time signal is quantized to discretize the continuous amplitude value of the discrete time signal and convert the voltage value of the sampling point into a series of discrete digital values. The accuracy of quantization depends on the number of bits selected. Common numbers of bits are 16 bits and 24 bits, which are used for standard and high-fidelity audio signals, respectively. The higher the number of bits, the higher the accuracy of quantization, and the dynamic range and sound quality of the audio signal are improved accordingly. Through quantization, the discrete time signal is converted into digital audio data. Fast Fourier transform is performed on the digital audio data to convert the time domain representation of the audio signal into frequency components, revealing the amplitude and phase information of each frequency in the signal. Fast Fourier transform is used to decompose complex time domain signals into a superposition of several sine waves, each of which has a specific frequency, amplitude and phase. Perform spectrum analysis on frequency domain signals. Spectral analysis is used to study the frequency characteristics of signals. Spectral analysis can show the amplitude distribution of different frequency components. The frequency energy distribution of audio signals is reflected through the spectrum diagram. Through spectrum analysis, the energy distribution of different frequencies in the signal is identified to help identify the sources of errors that affect audio quality. These errors may come from noise, harmonic distortion or interference during transmission. Based on the results of spectrum analysis, error identification is performed to find abnormal frequency components or distortion components in the signal and determine the target error sources that affect audio quality. For example, if certain frequency components that should not exist or excessive harmonic components are found in the spectrum, these may be caused by distortion. Through error analysis, the specific error type and source are determined. Based on the error source, the basic structure of the waveform equation of the audio signal is determined. The basic structure of the waveform equation usually includes three parts: fundamental frequency component, harmonic component and DC bias. The fundamental frequency component represents the main frequency of the signal, that is, the main component of the audio signal, which is usually the core part of the tone. The harmonic component is an integer multiple of the fundamental frequency and reflects the timbre characteristics of the audio signal. The DC bias is a constant term in the signal, which represents the deviation of the average value of the signal, and is usually manifested as an overall rise or fall in the actual signal. The fundamental frequency component is parameterized. The amplitude A of the fundamental frequency represents the strength of the signal, and the angular frequency ω determines the frequency of the signal, and its relationship with the fundamental frequency f is ω=2πf. The phase φ determines the initial position of the signal on the time axis. Through these parameters, the fundamental frequency component can be completely described as a sine wave form. Similarly, the harmonic component is parameterized. Each harmonic component can be expressed as an amplitude B n and phase φ nThe frequencies of the harmonics are integer multiples of the fundamental frequency, so their angular frequency is nω. The combination of these harmonics determines the complexity and timbre characteristics of the audio signal. The DC offset C is a constant that represents the overall offset of the signal. These parameters are combined to form a complete waveform equation that can describe the frequency, amplitude, phase, and offset of the audio signal.

[0057] In a specific embodiment, the process of executing step 102 may specifically include the following steps:

[0058] Performing random initialization processing on undetermined parameters in the waveform equation to obtain an initial parameter set, and performing filtering processing on the digital audio data based on the initial parameter set to obtain an initial filtered signal;

[0059] Performing difference calculation on the initial filtering signal and the digital audio data to obtain an initial residual signal, and performing square sum calculation on the initial residual signal to obtain an initial residual square sum;

[0060] The initial residual square sum is used as the initial value of the fitness function, and the initial fitness function is generated. Based on the initial fitness function, the waveform equation parameters are subjected to gradient descent processing to obtain an updated parameter set;

[0061] The coefficients of the adaptive filter are updated based on the updated parameter set to obtain an updated adaptive filter, and the digital audio data is filtered using the updated adaptive filter to obtain an updated filtered signal;

[0062] Performing difference calculation on the updated filter signal and the digital audio data to obtain an updated residual signal, and performing square sum calculation on the updated residual signal to obtain an updated residual square sum;

[0063] The updated residual square sum is compared with a preset threshold value, and if it is less than the preset threshold value, the current parameter set is determined as the optimal waveform equation parameter;

[0064] If it is greater than or equal to the preset threshold, the iteration continues, and the number of iterations is counted to obtain the iteration count value, and the iteration count value is compared with the preset maximum number of iterations. If the preset maximum number of iterations is reached, the iteration is terminated and the current parameter set is output as the optimal waveform equation parameters.

[0065] Specifically, the undetermined parameters in the waveform equation are randomly initialized to obtain an initial parameter set, which provides an initial value for the subsequent optimization algorithm. A filter is constructed based on the initial parameter set, and the input digital audio data is filtered. The audio data is processed using the waveform equation in the initial parameter set to generate an initial filtered signal. The initial filtered signal is differenced with the original digital audio data. The difference reflects the error between the filtered signal and the original signal, that is, the initial residual signal. The residual signal indicates the degree of deviation between the filtered signal and the original signal. In order to quantify the error, the initial residual signal is squared and the initial residual sum of squares is obtained. The residual sum of squares R is one of the criteria for measuring the filtering effect and can be expressed by the following formula:

[0066]

[0067] Among them, y i is the i-th sample of the original digital audio data, is the i-th sample of the corresponding filtered signal, and N represents the total number of data samples. The initial residual sum of squares is used as the initial value of the fitness function to evaluate the quality of the current parameter set. The fitness function plays a guiding role in the optimization process. The goal is to minimize the value of the fitness function and thus optimize the parameter set. Based on the initial fitness function, the gradient descent method is applied to optimize the parameters of the waveform equation. The gradient descent method calculates the gradient of the fitness function relative to each parameter, and then updates the parameters in the opposite direction of the gradient to gradually approach the optimal solution. The update of the parameters can be expressed as:

[0068]

[0069] Among them, θ old is the current parameter, η is the learning rate, which indicates the step size of parameter update, is the partial derivative of the residual sum of squares R with respect to the parameter θ. In this way, the parameters are gradually adjusted, with the goal of reducing the residual sum of squares so that the filter better matches the input audio signal. The coefficients of the adaptive filter are updated based on the updated parameter set to generate a new adaptive filter. The filter uses the new parameter set to filter the digital audio data and generate an updated filtered signal, thereby detecting whether the parameter update reduces the error, that is, whether the deviation between the filtered signal and the original audio signal is reduced. The updated filtered signal is again differenced with the original audio data to obtain an updated residual signal. The updated residual signal is squared and summed to obtain an updated residual sum of squares. The updated residual sum of squares is compared with a preset threshold. If the updated residual sum of squares is less than the preset threshold, it means that the filtering effect is good enough and the parameter set has converged to the ideal value. At this time, the current parameter set is determined to be the optimal waveform equation parameter. If the updated residual sum of squares is greater than or equal to the preset threshold, it is necessary to continue iterating for optimization. During each iteration, the number of iterations is counted and the number of optimization rounds that have been performed is recorded. In order to prevent falling into an infinite loop or exceeding a reasonable calculation time, the iteration count value is compared with the preset maximum number of iterations. If the number of iterations has reached the preset maximum value, the current parameter set is considered to be the optimal solution or close to the optimal solution, although its residual square sum may not completely reach the preset threshold, but at this time the system terminates the iteration and outputs the current parameter set as the optimal waveform equation parameters.

[0070] In a specific embodiment, the process of executing step 103 may specifically include the following steps:

[0071] Based on the harmonic component information in the optimal waveform equation parameters, the harmonic suppression filter parameters are calculated, and based on the harmonic suppression filter parameters, the digital audio data is filtered to obtain an audio signal after harmonic suppression;

[0072] Based on the background noise characteristics in the optimal waveform equation parameters, the adaptive noise canceller parameters are calculated, and based on the adaptive noise canceller parameters, the audio signal after harmonic suppression is subjected to noise elimination to obtain the audio signal after noise elimination;

[0073] Based on the dynamic range information in the optimal waveform equation parameters, the dynamic range adjuster parameters are calculated, and based on the dynamic range adjuster parameters, the dynamic range of the audio signal after noise elimination is adjusted to obtain an audio signal after dynamic range adjustment;

[0074] Based on the phase information in the optimal waveform equation parameters, a phase compensation lookup table is generated, and based on the phase compensation lookup table, phase correction is performed on the audio signal after the dynamic range is adjusted to obtain a phase-corrected audio signal;

[0075] Performing frequency response analysis on the phase-corrected audio signal to obtain a frequency response curve, and calculating corresponding equalizer parameters based on the frequency response curve;

[0076] Based on the equalizer parameters, the phase-corrected audio signal is equalized to obtain an optimized audio signal.

[0077] Specifically, the harmonic components are analyzed. Harmonic components are integer multiples of the fundamental frequency. In audio signals, these harmonics often cause distortion, especially when unnecessary high-order harmonics are strong. Based on the harmonic components in the optimal waveform equation, the filter parameters that suppress unnecessary harmonics are calculated. For example, let the fundamental frequency be f0, the angular frequency be ω0 = 2πf0, and suppose there are some high-order harmonics B n and φ n The sound quality is negatively affected. The pedal wave suppression filter can reduce the amplitude of unnecessary harmonics in the frequency domain. The filter design is based on the frequency position and amplitude of the harmonics. Usually, a band stop filter (also called a notch filter) is used to suppress a specific harmonic frequency band. The transfer function of the band stop filter can be expressed as:

[0078]

[0079] Among them, f n Represents the harmonic frequency that needs to be suppressed. By using this filter to filter the audio data, the interference of harmonics on the sound quality is effectively reduced, and an audio signal after harmonic suppression is obtained. Based on the background noise characteristics in the optimal waveform equation parameters, the adaptive noise eliminator parameters are calculated. An adaptive noise eliminator is a filter for removing noise, which dynamically adjusts the filter coefficients according to the time or frequency changes of the signal. Use an adaptive filtering algorithm, such as the LMS (least mean square) algorithm, to dynamically adjust the filter parameters by minimizing the error between the output signal and the desired signal. The formula is expressed as:

[0080] e(n)=d(n)-y(n);

[0081] Where e(n) is the error signal, d(n) is the desired signal, and y(n) is the filter output. The parameters of the noise canceller are optimized by minimizing e(n). After calculating these parameters, the adaptive noise canceller is applied to the audio signal after harmonic suppression to obtain the audio signal after noise elimination. According to the dynamic range characteristics of the audio signal, the parameters of the dynamic range adjuster are calculated. The dynamic range adjuster is used to balance the different volume parts of the audio signal to ensure that the weak parts can be boosted while the strong parts do not exceed the normal range. This process is usually achieved through a compressor or expander. The compressor controls the signal peak by reducing the gain of the high-volume signal. The formula is:

[0082]

[0083] Where x(t) is the input signal, y(t) is the output signal, T is the threshold, and R is the compression ratio. By adjusting these parameters, the dynamic range adjuster can boost weaker signals while controlling stronger signals to avoid clipping distortion, and obtain an audio signal with dynamic range adjustment. A phase compensation lookup table is generated based on the phase information in the optimal waveform equation. For example, if the phase φ of a certain frequency component n If an offset occurs, it can be corrected by using the corresponding compensation value in the lookup table to restore the phase to the ideal state. Using the lookup table to perform phase correction on the audio signal after dynamic range adjustment can restore the original phase relationship of the audio signal and improve the fidelity of the sound quality. Perform frequency response analysis on the phase-corrected audio signal to obtain the amplitude response of the audio signal at different frequencies. By analyzing the frequency response curve, identify which frequencies need to be strengthened or weakened. Usually, an unbalanced frequency response will lead to deviations in sound quality, such as too strong low frequencies or unclear high frequencies. Based on the results of the frequency response analysis, the equalizer parameters are calculated, and the equalizer optimizes the frequency distribution of the audio signal by adjusting the gains of different frequency bands. The typical transfer function of the equalizer can be expressed as:

[0084] H(f)=G l (f)+G m (f)+G h (f);

[0085] Among them, G l (f), G m (f), G h (f) represents the gains of low frequency, medium frequency and high frequency respectively. By adjusting these gains, the frequency response curve is made flatter and the frequency equalization is achieved. Based on the equalizer parameters, the equalizer is applied to equalize the audio signal after phase correction, and finally the optimized audio signal is obtained.

[0086] In a specific embodiment, the process of executing step 104 may specifically include the following steps:

[0087] Divide the optimized audio signal into time windows to obtain multiple audio data segments with fixed durations, and perform frame synchronization and frame header addition on each audio data segment to obtain multiple audio data packets;

[0088] Priority is assigned to multiple audio data packets to obtain a priority value of each audio data packet;

[0089] Based on the priority value of the audio data packet and the network transmission conditions, a wireless audio transmission optimization model with time window constraints is constructed, and a compact transmission sequence algorithm is applied to the wireless audio transmission optimization model to obtain an initial transmission sequence;

[0090] Based on the initial transmission sequence, the audio data packets are cyclically linearly exchanged to obtain multiple candidate transmission sequences;

[0091] Multiple candidate transmission sequences are evaluated to obtain an optimal transmission sequence, and based on the optimal transmission sequence, a transmission time window is allocated to each audio data packet to obtain a transmission scheduling scheme.

[0092] Specifically, the optimized audio signal is divided into time windows, and the continuous audio signal is divided into several smaller audio data segments according to a fixed duration. Frame synchronization and frame header addition are performed on each audio data segment. Frame synchronization is achieved by appending specific frame header information before each audio data segment. The frame header usually contains information such as synchronization identifier, data packet length, and timestamp, so that the receiving end can accurately identify the start and end positions of the data packet. Through frame synchronization and frame header addition, the continuous audio signal is cut into multiple organized audio data packets. According to the importance and real-time requirements of the audio signal, priority is assigned to multiple audio data packets to obtain the priority value of each audio data packet. For example, the key part of the voice conversation may be assigned a higher priority to ensure its priority transmission, while the secondary background sound may be assigned a lower priority. A wireless audio transmission optimization model with time window constraints is constructed based on the priority value of the audio data packet and the network transmission conditions, so as to optimize the transmission efficiency of audio data in the wireless network while ensuring real-time performance. Network transmission conditions include factors such as bandwidth, delay, and packet loss rate, while the time window constraint means that each data packet must be transmitted within a specific time window to avoid user experience problems caused by audio transmission delay. The goal of the model is to minimize the transmission time of audio packets while maximizing the utilization of network resources. In order to solve the transmission optimization model, the compact transmission sequence algorithm is used. The compact transmission sequence algorithm is an algorithm for optimizing the transmission order of data packets. The algorithm generates an initial transmission sequence based on the priority of each audio packet and network conditions. In terms of formula, the transmission sequence can be expressed as T s To express it, the optimization goal of the initial transmission sequence is:

[0093]

[0094] Among them, P i Indicates the priority value of the ith data packet, D iRepresents the transmission delay of the i-th data packet, and N represents the total number of data packets. The optimization goal is to minimize the weighted sum of priority and delay to ensure that high-priority data packets are transmitted with the lowest delay. Based on the initial transmission sequence, the audio data packets are cyclically linearly exchanged. Cyclic linear exchange is a strategy for optimizing the transmission order, which searches for better candidate transmission sequences by different arrangements and exchange transmission orders. By exchanging the transmission order between multiple data packets, multiple different candidate transmission sequences are obtained. Multiple candidate transmission sequences are evaluated. The evaluation criteria include the total transmission time of each sequence, the packet loss rate, and the utilization of network resources. The evaluation process selects the best performing sequence by comparing the performance of different candidate transmission sequences. Based on the optimal transmission sequence, a transmission time window is allocated to each audio data packet, so that each data packet can be transmitted in an appropriate time period according to its priority value and network conditions. Through the optimized time window allocation, the entire audio transmission system can ensure the quality and real-time performance of the audio under limited bandwidth and complex network conditions.

[0095] In a specific embodiment, the execution step constructs a wireless audio transmission optimization model with time window constraints based on the priority value of the audio data packet and the network transmission condition, and applies a compact transmission sequence algorithm to the wireless audio transmission optimization model to process the wireless audio transmission optimization model. The process of obtaining the initial transmission sequence may specifically include the following steps:

[0096] Normalizing the priority value of the audio data packet to obtain a normalized priority value, and parameterizing the network transmission condition to obtain a network parameter;

[0097] Based on the normalized priority value and the network parameters, an objective function is constructed, wherein the objective function is to minimize the weighted sum of the total transmission time and the packet loss rate;

[0098] The transmission time of the audio data packet is constrained to obtain a time window constraint condition, and the objective function and the time window constraint condition are combined to obtain a wireless audio transmission optimization model;

[0099] The transmission optimization model of wireless audio is transformed into a linear programming model to obtain a linear programming model, and based on the linear programming model, a compact transmission sequence algorithm is constructed, where the compact transmission sequence algorithm includes three sub-processes: initialization, sequence generation and optimization;

[0100] The audio data packets are sorted according to the earliest possible transmission time to obtain an initial sequence, and based on the initial sequence, a sequence generation subprocess of a compact transmission sequence algorithm is applied to obtain a set of candidate transmission sequences;

[0101] Apply the optimization subprocess of the compact transmission sequence algorithm to the candidate transmission sequence set to obtain the initial transmission sequence.

[0102] Specifically, the priority value of each audio data packet is normalized, and the priority values ​​of different data packets are converted into a standardized range, usually between [0, 1], to obtain the normalized priority value. The network transmission conditions are parameterized. Network transmission conditions include multiple parameters such as bandwidth, delay, and packet loss rate. Assuming that the network bandwidth is B, the delay is D, and the packet loss rate is L, these network parameters can be obtained through real-time monitoring or historical data. Based on the normalized priority value and network parameters, an objective function is constructed to minimize the weighted sum of the total transmission time and the packet loss rate, ensuring that the transmission process can not only ensure the rapid transmission of important data packets, but also effectively reduce the risk of data packet loss. The objective function can be expressed as:

[0103]

[0104] Among them, T i represents the transmission time of the ith data packet, L i represents the packet loss rate of the i-th data packet, w1 and w2 are the weight coefficients of transmission time and packet loss rate respectively, and N is the total number of audio data packets. By adjusting the values ​​of w1 and w2, different trade-offs can be made according to the actual application requirements. For example, in real-time audio transmission, more attention may be paid to the transmission time, so the value of w1 may be larger, while in non-real-time transmission, more attention may be paid to the packet loss rate. In order to improve the practicality of the model, the transmission time of the audio data packet is constrained to obtain the time window constraint. The time window constraint stipulates that each data packet must be transmitted within a specific time window to ensure the real-time nature of the audio signal. This can be expressed as:

[0105] T i ≤T max ;

[0106] Among them, T max is the maximum allowed transmission time window. Combining the objective function with the time window constraint, we get the transmission optimization model of wireless audio. This model comprehensively considers the transmission time, packet loss rate and time window constraint to ensure that the loss and delay of data packets are minimized while meeting the real-time transmission requirements. In order to solve the transmission optimization model, it is converted into a linear programming model. Linear programming is a commonly used optimization method. By converting nonlinear problems into linear form, it can be solved more efficiently. The converted linear programming model can be expressed as:

[0107]

[0108] Among them, x i is a decision variable indicating whether to select data packet i for transmission. i =1 means select transmission, x i=0 means no transmission. After linear programming conversion, the standard linear programming solution algorithm is used to optimize the transmission sequence. A compact transmission sequence algorithm is constructed based on the linear programming model. The algorithm includes three main sub-processes: initialization, sequence generation and optimization. In the initialization stage, the initial transmission parameters and network conditions are set. The sequence generation sub-process generates the initial transmission sequence according to the normalized priority value and network conditions to ensure that high-priority data packets can be transmitted first. The optimization sub-process adjusts the generated transmission sequence to ensure maximum utilization of network bandwidth and reduce transmission delay and packet loss rate. The audio data packets are sorted according to the earliest possible transmission time to obtain the initial sequence. The sorting basis of the initial sequence is the priority value and network conditions of each data packet. Data packets with high priority and early transmission time are arranged at the front of the sequence. Assume that the priorities of the data packets in the initial sequence are P1, P2,…, P N , through the sequence generation subprocess of the compact transmission sequence algorithm, a set of transmission sequences is initially generated. After the initial sequence is generated, the optimization subprocess of the algorithm adjusts the sequence. By cyclically exchanging the positions of data packets, the optimization subprocess can find multiple candidate transmission sequences. Each candidate sequence is arranged in a different order of data packets to ensure that high-priority data packets are transmitted with the lowest possible delay while meeting the time window constraints. The set of candidate transmission sequences is evaluated, and the optimal transmission sequence is selected by comparing the performance of different sequences. The evaluation criteria usually include total transmission time, packet loss rate, and bandwidth utilization. By analyzing these criteria, it is determined which sequence has the best transmission performance under the current network conditions. Based on the optimal transmission sequence obtained by the evaluation, a transmission time window is allocated to each audio data packet to complete the final transmission scheduling.

[0109] In a specific embodiment, the process of executing step 105 may specifically include the following steps:

[0110] Performing content analysis on multiple audio data packets to obtain audio type information, and selecting a corresponding encoding algorithm for each audio data packet based on the audio type information to obtain an encoding algorithm set;

[0111] Encode multiple audio data packets based on a set of encoding algorithms to obtain encoded audio streams, and layer the encoded audio streams to obtain basic layer and enhanced layer audio data;

[0112] Based on the transmission scheduling scheme, the base layer audio data is modulated to obtain a base layer OFDM symbol;

[0113] Segmenting the enhanced layer audio data to obtain a plurality of enhanced layer data segments, and modulating the plurality of enhanced layer data segments based on a transmission scheduling scheme to obtain a plurality of enhanced layer OFDM symbols;

[0114] Power is allocated to a base layer OFDM symbol and multiple enhancement layer OFDM symbols to obtain a power allocation scheme, and based on the power allocation scheme, the base layer OFDM symbol and multiple enhancement layer OFDM symbols are combined to obtain multi-layer OFDM symbols.

[0115] Specifically, content analysis is performed on multiple audio data packets to obtain audio type information of each audio data packet. Audio type information refers to the nature of audio, such as speech, music, background sound, etc. For example, speech signals usually contain low-frequency and medium-frequency components and have high requirements for clarity, while music signals contain rich frequency information and emphasize high fidelity and dynamic range. A corresponding encoding algorithm is selected for each audio data packet based on the audio type information. Different audio types are suitable for different encoding methods. For example, speech signals can select encoding algorithms with higher compression rates and retain clarity, such as adaptive multi-rate codec, while music signals can select encoding algorithms that retain more frequency information, such as advanced audio coding. These encoding algorithms can retain the core information of the audio with as little data as possible. By analyzing the type of each audio data packet, a coding algorithm is selected in a targeted manner to obtain a set of coding algorithms. Based on the set of coding algorithms, multiple audio data packets are encoded to obtain encoded audio streams. The encoding process compresses and processes the original audio data so that the audio data can be efficiently transmitted over the network. The encoded audio stream is layered to optimize the transmission of audio data of different priorities. Audio streams are usually divided into two layers: a basic layer and an enhanced layer. The base layer contains the core information of the audio signal, while the enhancement layer contains more sound quality details. Based on the transmission scheduling scheme, the base layer audio data is modulated to obtain the base layer OFDM (Orthogonal Frequency Division Multiplexing) symbol. OFDM is a multi-carrier modulation technology that can effectively utilize bandwidth and reduce signal interference by dividing data into multiple subcarriers for transmission. The generation of OFDM symbols can be achieved by performing QAM (Quadrature Amplitude Modulation) or PSK (Phase Shift Keying) modulation on the audio data. Assuming QAM is used for modulation, the base layer OFDM symbol can be expressed as:

[0116]

[0117] Among them, X k is the base layer OFDM symbol, A k is the amplitude, indicating the magnitude of the signal, θ kis the phase, indicating the phase of the signal. In this way, the base layer audio data is converted into OFDM symbols suitable for wireless transmission. The enhanced layer audio data is segmented and divided into multiple enhanced layer data segments, each of which contains part of the information of the enhanced layer audio. Based on the transmission scheduling scheme, the enhanced layer data segments are modulated to obtain multiple enhanced layer OFDM symbols. The enhanced layer OFDM symbols, like the base layer symbols, can achieve efficient data transmission through OFDM modulation. Power is allocated to the base layer OFDM symbols and multiple enhancement layer OFDM symbols to ensure that the transmission quality of the base layer symbols is prioritized under the limited total transmission power, and reasonable remaining power is allocated to the enhancement layer symbols. The power allocation scheme can be expressed by the following formula:

[0118]

[0119] Among them, P total is the total power, P basic is the power allocated to the base layer OFDM symbol, P enhance,n is the power of the nth enhancement layer OFDM symbol. By allocating these powers reasonably, the base layer is ensured to obtain sufficient energy for transmission, and the enhancement layer dynamically adjusts the power according to the network conditions. Based on the power allocation scheme, the base layer OFDM symbol and multiple enhancement layer OFDM symbols are combined to obtain multi-layer OFDM symbols.

[0120] In a specific embodiment, the process of executing step 106 may specifically include the following steps:

[0121] Removing cyclic prefixes from the received multi-layer OFDM symbols to obtain a pure OFDM symbol sequence, and transforming the pure OFDM symbol sequence to obtain a frequency domain signal;

[0122] Based on a preset power allocation scheme, the frequency domain signal is demodulated in layers to obtain a base layer signal and multiple enhancement layer signals, and the base layer signal and the multiple enhancement layer signals are respectively channel equalized to obtain equalized multi-layer signals;

[0123] Based on a preset set of encoding algorithms, the equalized multi-layer signals are decoded to obtain decoded basic layer audio data and enhanced layer audio data;

[0124] Merging the decoded basic layer audio data and enhanced layer audio data to obtain complete decoded audio data;

[0125] The complete decoded audio data is reassembled into data packets to obtain a reconstructed digital audio data stream, and the reconstructed digital audio data stream is converted from digital to analog to obtain a reconstructed analog audio signal.

[0126] Specifically, the cyclic prefix of the received multi-layer OFDM symbols is removed. The cyclic prefix is ​​added to reduce the impact of multipath interference, but at the receiving end, it is a redundant part and needs to be removed in subsequent processing. The cyclic prefix is ​​usually a copy of the end of the OFDM symbol. After removing it, a pure OFDM symbol sequence is obtained. Assuming that the length of the OFDM symbol is N and the cyclic prefix length is L, at the receiving end, by discarding the first L samples, the remaining N samples are the original OFDM symbol. Perform Fourier transform on the pure OFDM symbol sequence, converting it from the time domain to the frequency domain to obtain a frequency domain signal. The Fourier transform decomposes the received time domain signal into components of different frequencies to facilitate subsequent signal processing. The Fourier transform formula is as follows:

[0127]

[0128] Among them, X k is the kth frequency domain signal, x n is the nth sample in the time domain, and N is the length of the fast Fourier transform. After the fast Fourier transform, the receiver can analyze the amplitude and phase of each frequency component to obtain the representation of the OFDM symbol in the frequency domain. Based on the preset power allocation scheme, the frequency domain signal is demodulated in layers to restore the received signal into audio data at different levels. The demodulation process uses the preset power allocation scheme to separate the frequency domain signal according to the power distribution of signals at different levels to obtain a basic layer signal and multiple enhancement layer signals. The basic layer signal and the enhancement layer signal are channel equalized separately to compensate for the distortion of the signal caused by channel fading, interference and other factors during wireless transmission. Commonly used channel equalization methods include minimum mean square error equalization and zero forced equalization. Channel equalization uses the channel state information of the receiver to estimate the impact of the channel and restores the original state of the signal through inverse operations. Channel equalization can be expressed as:

[0129]

[0130] in, is the equalized signal, X k is the received frequency domain signal, H kis the channel frequency response. By performing equalization processing on the base layer and the enhancement layer respectively, an equalized multi-layer signal is obtained. Based on a preset set of coding algorithms, the equalized multi-layer signal is decoded. Different audio layers usually use different coding algorithms. For example, the base layer audio data may use a more efficient and reliable coding method, such as AMR coding, while the enhancement layer audio data may use a more complex coding method such as AAC to ensure sound quality. The decoding process needs to decode the base layer and enhancement layer signals respectively according to the preset set of coding algorithms to obtain decoded base layer audio data and enhancement layer audio data. The decoded base layer audio data and enhancement layer audio data are merged to restore the complete audio signal. The complete decoded audio data is packet reassembled, and the scattered data packets are reassembled into a continuous audio data stream to obtain a reconstructed digital audio data stream. By checking the timestamp and sequence number of each data packet, it is ensured that all data packets are correctly sorted to avoid data loss or overlap. The reconstructed digital audio data stream is converted into an analog signal. The digital-to-analog converter converts the digital audio data stream into an analog signal, restores the original waveform, and obtains a reconstructed analog audio signal.

[0131] In a specific embodiment, the execution step is based on a preset power allocation scheme, hierarchically demodulating the frequency domain signal to obtain a base layer signal and multiple enhancement layer signals, and performing channel equalization on the base layer signal and the multiple enhancement layer signals respectively, and the process of obtaining the equalized multi-layer signal can specifically include the following steps:

[0132] Performing power spectrum analysis on the frequency domain signal to obtain a signal power distribution diagram, and performing subcarrier allocation on the frequency domain signal based on the signal power distribution diagram to obtain a basic layer subcarrier set and multiple enhancement layer subcarrier sets;

[0133] Demodulating a base layer subcarrier set to obtain a base layer signal, and demodulating multiple enhancement layer subcarrier sets respectively to obtain multiple enhancement layer signals;

[0134] Based on the pre-estimated channel state information, channel estimation is performed on the base layer signal to obtain a base layer channel estimation result, and based on the base layer channel estimation result, equalization is performed on the base layer signal to obtain an equalized base layer signal;

[0135] Based on the pre-estimated channel state information and the basic layer channel estimation result, channel estimation is performed on multiple enhancement layer signals respectively to obtain multiple enhancement layer channel estimation results;

[0136] Based on the multiple enhancement layer channel estimation results, the multiple enhancement layer signals are equalized respectively to obtain multiple equalized enhancement layer signals;

[0137] The equalized base layer signal and multiple equalized enhancement layer signals are combined to obtain an equalized multi-layer signal.

[0138] Specifically, the frequency domain signal is subjected to power spectrum analysis. Power spectrum analysis is a frequency domain analysis technique that can reveal the energy distribution of each frequency component in the signal. Through power spectrum analysis, the power density of each frequency, that is, the strength of the signal at different frequencies, can be understood. Assuming there is a frequency domain signal X(f) after Fourier transformation, its power spectrum can be calculated by the following formula:

[0139] P(f)=|X(f)| 2 ;

[0140] Among them, P(f) represents the power density at frequency f, and X(f) is the frequency domain signal at the corresponding frequency f. By calculating the power spectrum, the power distribution diagram of the signal is drawn to show the energy distribution of the signal at different frequencies. Based on the signal power distribution diagram, subcarrier allocation is performed on the frequency domain signal, and the frequency domain signal is allocated to different layers to meet the needs of multi-layer transmission. Generally, frequencies with higher power are preferentially allocated to the basic layer signal, because the basic layer signal undertakes the transmission task of core information and requires higher reliability. Frequencies with lower power can be allocated to the enhancement layer signal, which is used to improve the sound quality and has higher fault tolerance. Through the subcarrier allocation process, the frequency range is divided into a basic layer subcarrier set and multiple enhancement layer subcarrier sets. The basic layer subcarrier set is demodulated to obtain the basic layer signal. Demodulation is the process of restoring the modulated OFDM symbol to the original digital data. In the demodulation process, the receiving end uses the received subcarrier data to convert the frequency domain signal back to the time domain signal through methods such as inverse Fourier transform. For the enhancement layer subcarrier set, it is demodulated separately to obtain multiple enhancement layer signals. These enhancement layer signals represent the audio detail information in different enhancement layers. Channel estimation is performed on the base layer signal based on the pre-estimated channel state information. The channel state information contains information such as the fading characteristics, noise and interference of the channel, reflecting the changes of the signal during the transmission process. Channel estimation can be achieved through the training sequence known to the receiving end. By analyzing the transmission characteristics of the training sequence, the receiving end can infer the influence of the channel. According to the channel estimation result of the base layer, the base layer signal is channel equalized to eliminate the distortion caused by the channel to the signal and restore the original form of the signal. The channel equalizer processes the received signal to reduce the amplitude and phase distortion caused by channel fading. The equalized base layer signal restores the main information part of the original audio signal. Based on the pre-estimated channel state information and the base layer channel estimation result, channel estimation is performed on each enhancement layer signal. The enhancement layer signal is affected by channel fading and noise during the transmission process, and the transmission characteristics of the signal need to be analyzed through channel estimation. Based on the channel estimation result of each enhancement layer, channel equalization is performed on each enhancement layer signal. The equalized enhancement layer signal restores more details of the audio signal, such as high-frequency sound effects, stereo details, etc. The role of the enhancement layer is to improve the overall quality of the audio signal and ensure that higher sound quality can be provided under better network conditions. Through channel equalization, the distortion of the enhancement layer signal is effectively compensated to ensure the integrity of the signal. The equalized basic layer signal and multiple enhancement layer signals are combined to obtain the final multi-layer signal. The basic layer signal provides the core information of the audio, ensuring that important content can be transmitted even under poor network conditions; the enhancement layer signal enriches the audio details. When the network conditions permit, the enhancement layer signal can provide users with a high-fidelity audio experience.

[0141] The wireless audio data transmission and reading method in the embodiment of the present invention is described above. The audio playback device in the embodiment of the present invention is described below. Figure 2 , an embodiment of the audio playback device in the embodiment of the present invention includes:

[0142] The signal processing module 201 is used to perform signal processing on the input original audio signal to obtain digital audio data, and perform transformation processing on the digital audio data to obtain a frequency domain signal, and establish a waveform equation based on the frequency domain signal;

[0143] The parameter optimization module 202 is used to optimize the parameters of the waveform equation to obtain the optimized waveform equation parameters, construct a fitness function based on the optimized waveform equation parameters, and optimize the fitness function to obtain the optimal waveform equation parameters;

[0144] A creation module 203 is used to create a signal processor based on the optimal waveform equation parameters, and process the digital audio data through the signal processor to obtain an optimized audio signal;

[0145] A solution module 204 is used to divide the optimized audio signal into data to obtain multiple audio data packets, establish a transmission optimization model based on the audio data packets, and solve the transmission optimization model to obtain a transmission scheduling solution;

[0146] The encoding module 205 is used to encode multiple audio data packets based on the transmission scheduling scheme to obtain an encoded audio stream, and perform multi-layer mapping on the encoded audio stream to obtain multi-layer OFDM symbols;

[0147] The decoding module 206 is used to perform signal reconstruction processing on the multi-layer OFDM symbols to obtain a receiving end signal, decode the receiving end signal to obtain decoded audio data, and reorganize the decoded audio data to obtain a reconstructed digital audio signal.

[0148] Through the synergy of the above components, by establishing accurate waveform equations and optimizing parameters, this method can more accurately describe and process audio signals, effectively reduce signal distortion and improve audio quality. The use of multi-level signal processors, including harmonic suppression, noise elimination, dynamic range adjustment, etc., can comprehensively optimize audio signals and significantly improve sound quality. The introduction of transmission optimization models and compact transmission sequence algorithms achieves more efficient packet transmission scheduling, reduces transmission delay, and improves network resource efficiency. The use of multi-layer OFDM symbol mapping technology enhances the system's adaptability and anti-interference ability in complex network environments and improves transmission reliability. Through layered coding and decoding technology, hierarchical transmission of audio data is achieved, ensuring basic sound quality under bandwidth constraints and providing a higher quality audio experience when conditions permit. The signal reconstruction and equalization processing technology at the receiving end effectively compensates for channel distortion during transmission and improves the restoration of decoded audio. The overall solution has strong adaptability and scalability, can be adaptively adjusted according to different types of audio content and network conditions, and is suitable for a variety of application scenarios.

[0149] above Figure 2 The audio playback device in the embodiment of the present invention is described in detail from the perspective of modular functional entities, and the electronic device in the embodiment of the present invention is described in detail from the perspective of hardware processing.

[0150] Figure 3 3 is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. The electronic device 300 may have relatively large differences due to different configurations or performances, and may include one or more processors (central processing units, CPU) 310 (for example, one or more processors) and a memory 320, and one or more storage media 330 (for example, one or more mass storage device terminals) storing application programs 333 or data 332. Among them, the memory 320 and the storage medium 330 can be short-term storage or permanent storage. The program stored in the storage medium 330 may include one or more modules (not shown in the figure), and each module may include a series of instruction operations in the electronic device 300. Furthermore, the processor 310 can be configured to communicate with the storage medium 330, and execute a series of instruction operations in the storage medium 330 on the electronic device 300 to implement the steps of the above-mentioned wireless audio data transmission and reading method.

[0151] The electronic device 300 may also include one or more power supplies 340, one or more wired or wireless network interfaces 350, one or more input and output interfaces 360, and / or one or more operating systems 331, such as Windows Server, Mac OS X, Unix, Linux, FreeBSD, etc. It will be appreciated by those skilled in the art that Figure 3 The structure of the electronic device shown does not constitute a limitation on the electronic device provided by the present invention, and may include more or less components than shown in the figure, or combine certain components, or arrange the components differently.

[0152] The present invention also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. Instructions are stored in the computer-readable storage medium. When the instructions are executed on a computer, the computer executes the steps of the wireless audio data transmission and reading method.

[0153] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems, systems and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0154] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc., various media that can store program codes.

[0155] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features thereof may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A wireless audio data transmission and reading method, characterized in that: The method comprises: Performing signal processing on the input original audio signal to obtain digital audio data, and performing transformation processing on the digital audio data to obtain a frequency domain signal, and establishing a waveform equation based on the frequency domain signal; Optimizing the waveform equation parameters to obtain optimized waveform equation parameters, constructing a fitness function based on the optimized waveform equation parameters, and optimizing the fitness function to obtain optimal waveform equation parameters; Creating a signal processor based on the optimal waveform equation parameters, and processing the digital audio data by the signal processor to obtain an optimized audio signal; Performing data division on the optimized audio signal to obtain a plurality of audio data packets, establishing a transmission optimization model based on the audio data packets, and solving the transmission optimization model to obtain a transmission scheduling solution; Based on the transmission scheduling scheme, the multiple audio data packets are encoded to obtain an encoded audio stream, and the encoded audio stream is multi-layer mapped to obtain multi-layer OFDM symbols; The multi-layer OFDM symbols are subjected to signal reconstruction processing to obtain a receiving end signal, the receiving end signal is decoded to obtain decoded audio data, and the decoded audio data is reorganized to obtain a reconstructed digital audio signal.

2. The wireless audio data transmission and reading method according to claim 1, characterized in that: The method of performing signal processing on the input original audio signal to obtain digital audio data, performing transformation processing on the digital audio data to obtain a frequency domain signal, and establishing a waveform equation based on the frequency domain signal includes: Sampling the original audio signal to obtain a discrete time signal, and quantizing the discrete time signal to obtain the digital audio data; Performing fast Fourier transform on the digital audio data to obtain the frequency domain signal; Performing spectrum analysis on the frequency domain signal to obtain spectrum characteristics of the signal, and performing error identification based on the spectrum characteristics to obtain target error sources that affect audio quality; Determining a basic structure of a waveform equation based on the target error source, the basic structure comprising a fundamental frequency component, a harmonic component, and a DC bias; The fundamental frequency component is parameterized to obtain parameters representing the fundamental frequency amplitude, angular frequency and phase; the harmonic component is parameterized to obtain parameters representing the amplitude and phase of each order of harmonics; the DC bias is parameterized to obtain parameters representing the bias value; The parameters representing the fundamental frequency amplitude, angular frequency and phase, the parameters representing the amplitude and phase of each order harmonic, and the parameter representing the offset value are combined to obtain a waveform equation containing multiple parameters to be determined, wherein the waveform equation is: y(t)=A*sin(ωt+φ)+Σ(Bn*sin(nωt+φn))+C; Where y(t) represents the waveform equation, A is the fundamental frequency amplitude, ω is the angular frequency, φ is the phase, Bn and φn are the amplitude and phase of the harmonic components respectively, and C is the DC bias.

3. The wireless audio data transmission and reading method according to claim 1, characterized in that: The step of optimizing the waveform equation parameters to obtain optimized waveform equation parameters, constructing a fitness function based on the optimized waveform equation parameters, and optimizing the fitness function to obtain optimal waveform equation parameters includes: Performing random initialization processing on undetermined parameters in the waveform equation to obtain an initial parameter set, and performing filtering processing on the digital audio data based on the initial parameter set to obtain an initial filtered signal; Performing difference calculation on the initial filtered signal and the digital audio data to obtain an initial residual signal, and performing square sum calculation on the initial residual signal to obtain an initial residual square sum; The initial residual square sum is used as the initial value of the fitness function, and an initial fitness function is generated. Based on the initial fitness function, a gradient descent process is performed on the waveform equation parameters to obtain an updated parameter set; Update the coefficients of the adaptive filter based on the updated parameter set to obtain an updated adaptive filter, and use the updated adaptive filter to filter the digital audio data to obtain an updated filtered signal; Performing difference calculation on the updated filtered signal and the digital audio data to obtain an updated residual signal, and performing square sum calculation on the updated residual signal to obtain an updated residual square sum; The updated residual square sum is compared with a preset threshold value, and if it is less than the preset threshold value, the current parameter set is determined as the optimal waveform equation parameter; If it is greater than or equal to the preset threshold, the iteration continues, and the number of iterations is counted to obtain an iteration count value, and the iteration count value is compared with the preset maximum number of iterations. If the preset maximum number of iterations is reached, the iteration is terminated and the current parameter set is output as the optimal waveform equation parameters.

4. The wireless audio data transmission and reading method according to claim 1, characterized in that: The step of creating a signal processor based on the optimal waveform equation parameters and processing the digital audio data by the signal processor to obtain an optimized audio signal includes: Calculating harmonic suppression filter parameters based on the harmonic component information in the optimal waveform equation parameters, and filtering the digital audio data based on the harmonic suppression filter parameters to obtain an audio signal after harmonic suppression; Calculating adaptive noise canceller parameters based on background noise features in the optimal waveform equation parameters, and performing noise elimination on the harmonically suppressed audio signal based on the adaptive noise canceller parameters to obtain a noise eliminated audio signal; Calculating dynamic range adjuster parameters based on dynamic range information in the optimal waveform equation parameters, and performing dynamic range adjustment on the audio signal after noise elimination based on the dynamic range adjuster parameters to obtain an audio signal after dynamic range adjustment; Based on the phase information in the optimal waveform equation parameters, a phase compensation lookup table is generated, and based on the phase compensation lookup table, phase correction is performed on the audio signal after the dynamic range is adjusted to obtain a phase-corrected audio signal; Performing frequency response analysis on the phase-corrected audio signal to obtain a frequency response curve, and calculating corresponding equalizer parameters based on the frequency response curve; Based on the equalizer parameters, the phase-corrected audio signal is equalized to obtain the optimized audio signal.

5. The wireless audio data transmission and reading method according to claim 1, characterized in that: The step of dividing the optimized audio signal into multiple audio data packets, establishing a transmission optimization model based on the audio data packets, and solving the transmission optimization model to obtain a transmission scheduling solution includes: Dividing the optimized audio signal into time windows to obtain a plurality of audio data segments of fixed duration, and performing frame synchronization and adding a frame header to each of the audio data segments to obtain a plurality of audio data packets; Allocating priorities for the multiple audio data packets to obtain a priority value for each audio data packet; Based on the priority value of the audio data packet and the network transmission condition, a transmission optimization model of wireless audio with time window constraints is constructed, and a compact transmission sequence algorithm is applied to the wireless audio transmission optimization model to obtain an initial transmission sequence; Based on the initial transmission sequence, cyclically linearly exchange the audio data packets to obtain multiple candidate transmission sequences; The multiple candidate transmission sequences are evaluated to obtain an optimal transmission sequence, and based on the optimal transmission sequence, a transmission time window is allocated to each audio data packet to obtain the transmission scheduling scheme.

6. The wireless audio data transmission and reading method according to claim 5, characterized in that: The step of constructing a wireless audio transmission optimization model with a time window constraint based on the priority value of the audio data packet and the network transmission condition, and applying a compact transmission sequence algorithm to the wireless audio transmission optimization model to obtain an initial transmission sequence includes: Normalizing the priority value of the audio data packet to obtain a normalized priority value, and parameterizing the network transmission condition to obtain a network parameter; Based on the normalized priority value and the network parameter, constructing an objective function, wherein the objective function is a weighted sum of minimizing a total transmission time and a packet loss rate; Performing constraint processing on the transmission time of the audio data packet to obtain a time window constraint condition, and combining the objective function and the time window constraint condition to obtain a transmission optimization model for the wireless audio; Performing linear programming transformation on the transmission optimization model of the wireless audio to obtain a linear programming model, and constructing a compact transmission sequence algorithm based on the linear programming model, wherein the compact transmission sequence algorithm includes three sub-processes of initialization, sequence generation and optimization; Sorting the audio data packets according to the earliest possible transmission time to obtain an initial sequence, and applying the sequence generation subprocess of the compact transmission sequence algorithm based on the initial sequence to obtain a candidate transmission sequence set; The optimization subprocess of the compact transmission sequence algorithm is applied to the candidate transmission sequence set to obtain the initial transmission sequence.

7. The wireless audio data transmission and reading method according to claim 1, characterized in that: The encoding process of the plurality of audio data packets based on the transmission scheduling scheme to obtain an encoded audio stream, and performing multi-layer mapping on the encoded audio stream to obtain multi-layer OFDM symbols includes: Performing content analysis on the multiple audio data packets to obtain audio type information, and selecting a corresponding encoding algorithm for each audio data packet based on the audio type information to obtain an encoding algorithm set; Encoding the multiple audio data packets based on the encoding algorithm set to obtain an encoded audio stream, and layering the encoded audio stream to obtain basic layer and enhanced layer audio data; Based on the transmission scheduling scheme, modulating the base layer audio data to obtain a base layer OFDM symbol; Segmenting the enhancement layer audio data to obtain a plurality of enhancement layer data segments, and modulating the plurality of enhancement layer data segments based on the transmission scheduling scheme to obtain a plurality of enhancement layer OFDM symbols; Power is allocated to the base layer OFDM symbol and the multiple enhancement layer OFDM symbols to obtain a power allocation scheme, and based on the power allocation scheme, the base layer OFDM symbol and the multiple enhancement layer OFDM symbols are merged to obtain multi-layer OFDM symbols.

8. The wireless audio data transmission and reading method according to claim 1, characterized in that: The signal reconstruction processing is performed on the multi-layer OFDM symbols to obtain a receiving end signal, the receiving end signal is decoded to obtain decoded audio data, and the decoded audio data is reorganized to obtain a reconstructed digital audio signal, including: Removing cyclic prefixes from received multi-layer OFDM symbols to obtain a pure OFDM symbol sequence, and transforming the pure OFDM symbol sequence to obtain a frequency domain signal; Based on a preset power allocation scheme, the frequency domain signal is demodulated in layers to obtain a base layer signal and multiple enhancement layer signals, and the base layer signal and the multiple enhancement layer signals are respectively channel equalized to obtain equalized multi-layer signals; Based on a preset set of encoding algorithms, the equalized multi-layer signals are decoded to obtain decoded basic layer audio data and enhanced layer audio data; Merging the decoded base layer audio data and enhanced layer audio data to obtain complete decoded audio data; The complete decoded audio data is reassembled into data packets to obtain a reconstructed digital audio data stream, and the reconstructed digital audio data stream is converted from digital to analog to obtain a reconstructed analog audio signal.

9. The wireless audio data transmission and reading method according to claim 8, characterized in that: The method of performing layered demodulation on the frequency domain signal based on a preset power allocation scheme to obtain a base layer signal and multiple enhancement layer signals, and performing channel equalization on the base layer signal and the multiple enhancement layer signals respectively to obtain equalized multi-layer signals includes: Performing power spectrum analysis on the frequency domain signal to obtain a signal power distribution diagram, and performing subcarrier allocation on the frequency domain signal based on the signal power distribution diagram to obtain a base layer subcarrier set and multiple enhancement layer subcarrier sets; Demodulating the base layer subcarrier set to obtain a base layer signal, and demodulating the multiple enhancement layer subcarrier sets respectively to obtain multiple enhancement layer signals; Based on the pre-estimated channel state information, perform channel estimation on the base layer signal to obtain a base layer channel estimation result, and based on the base layer channel estimation result, perform equalization on the base layer signal to obtain an equalized base layer signal; Based on the pre-estimated channel state information and the base layer channel estimation result, respectively perform channel estimation on the multiple enhancement layer signals to obtain multiple enhancement layer channel estimation results; Based on the multiple enhancement layer channel estimation results, respectively equalize the multiple enhancement layer signals to obtain multiple equalized enhancement layer signals; The equalized base layer signal and the multiple equalized enhancement layer signals are combined to obtain the equalized multi-layer signal.

10. An audio playback device, characterized in that: The device is used to perform the wireless audio data transmission and reading method according to any one of claims 1 to 9, and comprises: A signal processing module, used to perform signal processing on the input original audio signal to obtain digital audio data, and perform transformation processing on the digital audio data to obtain a frequency domain signal, and establish a waveform equation based on the frequency domain signal; A parameter optimization module, used to optimize the parameters of the waveform equation to obtain optimized waveform equation parameters, construct a fitness function based on the optimized waveform equation parameters, and optimize the fitness function to obtain optimal waveform equation parameters; A creation module, used to create a signal processor based on the optimal waveform equation parameters, and process the digital audio data through the signal processor to obtain an optimized audio signal; A solution module, configured to divide the optimized audio signal into data to obtain a plurality of audio data packets, establish a transmission optimization model based on the audio data packets, and solve the transmission optimization model to obtain a transmission scheduling solution; A coding module, configured to encode the plurality of audio data packets based on the transmission scheduling scheme to obtain an encoded audio stream, and perform multi-layer mapping on the encoded audio stream to obtain multi-layer OFDM symbols; The decoding module is used to perform signal reconstruction processing on the multi-layer OFDM symbols to obtain a receiving end signal, decode the receiving end signal to obtain decoded audio data, and reorganize the decoded audio data to obtain a reconstructed digital audio signal.

Images