A method for wireless audio data transmission and reading and an audio playback device

Abstract

The present invention relates to the technical field of audio processing, and discloses a method for wireless audio data transmission and reading and an audio playback device. Among them, the method includes: performing signal processing on the input original audio signal to obtain digital audio data and establishing a waveform equation; performing parameter optimization to obtain the optimal waveform equation parameters; creating a signal processor and performing processing to obtain an optimized audio signal; performing data partitioning to obtain multiple audio data packets, solving the transmission optimization model to obtain a transmission scheduling scheme; performing encoding processing to obtain an encoded audio stream and performing multi-layer mapping to obtain multi-layer OFDM symbols; performing signal reconstruction processing to obtain a received signal, performing decoding processing on the received signal to obtain decoded audio data and performing recombination to obtain a reconstructed digital audio signal. This method improves the performance of wireless audio data transmission and reading.

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 playback 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] Perform data partitioning on the optimized audio signal 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 scheme;

[0010] Based on the transmission scheduling scheme, perform encoding processing on the multiple audio data packets to obtain an encoded audio stream, and perform multi-layer mapping on the encoded audio stream to obtain multi-layer OFDM symbols;

[0011] Perform signal reconstruction processing on the multi-layer OFDM symbols to obtain a received signal, perform decoding processing on the received signal to obtain decoded audio data, and perform recombination on the decoded audio data to obtain a reconstructed digital audio signal.

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

[0013] A signal processing module, configured to perform signal processing on an 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;

[0014] A parameter optimization module, configured 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, configured 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 perform data partitioning on the optimized audio signal 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 scheme;

[0017] An encoding module, configured to perform encoding processing on the 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;

[0018] A decoding module, configured to perform signal reconstruction processing on the multi-layer OFDM symbols to obtain a received signal, perform decoding processing on the received signal to obtain decoded audio data, and perform recombination on the decoded audio data to obtain a reconstructed digital audio signal.

[0019] In a third aspect of the present invention, an electronic device is provided, including: a memory and at least one processor, wherein instructions are stored in the memory; the at least one processor invokes the instructions in the memory so that the electronic device executes the above-mentioned wireless audio data transmission and reading method.

[0020] In a fourth aspect of the present invention, a computer-readable storage medium is provided, wherein instructions are stored in the computer-readable storage medium, and when it runs on a computer, it enables 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, this method can more accurately describe and process audio signals, effectively reduce signal distortion, and improve audio quality. The use of a multi-stage signal processor, including harmonic suppression, noise cancellation, dynamic range adjustment, etc., can comprehensively optimize audio signals and significantly improve sound quality. The introduction of a transmission optimization model and a compact transmission sequence algorithm realizes a more efficient data packet transmission scheduling, reduces transmission delay, and improves the utilization rate of network resources. The use of a 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 hierarchical coding and decoding technologies, 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 degree 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 applicable to a variety of application scenarios.

[0022] Other features and advantages of the present invention will be described in the subsequent description, and, in part, will be obvious from the description, or will be understood by implementing the present invention. The objectives and other advantages of the present invention are achieved and obtained by the structures specifically pointed out in the description, the claims, and the drawings.

[0023] To make the above objectives, features, and advantages of the present invention more obvious and understandable, the following specific preferred embodiments are given, and in conjunction with the accompanying drawings, the detailed description is as follows. BRIEF DESCRIPTION OF THE DRAWINGS

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

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

[0026] Figure 3Schematic diagram of an embodiment of an electronic device in an embodiment of the present invention. Detailed implementation manners

[0027] To make the objectives, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Apparently, the described embodiments are some but not all of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0028] As used in the embodiments of the present invention, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally further includes other steps or units not listed, or optionally further includes other steps or units inherent to these processes, methods, products or devices.

[0029] To facilitate the understanding of this embodiment, first, a method for wireless audio data transmission and reading disclosed in the embodiments of the present invention will be introduced in detail. As Figure 1 shown, this method includes 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 can be understood that the execution subject of the present invention can be an audio playback device, or a terminal or a server. Specifically, no limitation is made here. In the embodiments of the present invention, the server is taken as an example of the execution subject for illustration.

[0032] Specifically, the original audio signal input is sampled. By sampling the audio signal at specific time intervals, a discrete-time signal is obtained. The discrete-time signal is quantized, and each sampling value of the discrete-time signal is converted into a digital form to obtain digital audio data, representing the amplitude of the sampling points as discrete values. The digital audio data is subjected to a fast Fourier transform to convert the time-domain signal into a frequency-domain signal, obtaining the frequency composition of the audio signal. The frequency-domain signal is subjected to spectral analysis to obtain the spectral characteristics of the signal, reflecting the amplitude distribution of each frequency component in the audio signal and their impact on the overall audio quality. By analyzing the spectral characteristics, the possible error sources in the signal are identified. Error identification helps to discover the specific problems that may cause a decline in audio quality, such as background noise, harmonic distortion, or signal loss. Based on the identified target error sources, the basic structure of the waveform equation is determined. This structure includes the fundamental frequency component, harmonic components, and DC bias. The fundamental frequency component is the main frequency component of the audio signal, usually representing the basic pitch of the sound, while the harmonic components are integer multiples of the fundamental frequency, and they jointly 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 offset 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 intensity of the main frequency of the signal, 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, and each harmonic has its unique amplitude and phase. The DC bias is parameterized to obtain a parameter representing the bias value. The magnitude of the DC bias affects the reference level of the signal and thus has an impact on the actual playback effect of the audio. The parameters of the fundamental frequency, the parameters of the harmonics, and the parameters of the DC bias are combined to form a waveform equation containing multiple undetermined parameters. The mathematical form of the waveform equation is:

[0033] where 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 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;

[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 through the parameters of the waveform equation to make it approximate the optimized signal as much as possible. The difference between the initial filtered signal and the original digital audio data is calculated to obtain an initial residual signal, which represents the deviation between the filtered signal and the original audio signal. By calculating the sum of squares of the initial residual signal, the initial sum of squared residuals is obtained, which reflects the error degree of the filtered signal under the current parameters. The initial sum of squared residuals is used as the initial value of the fitness function, and an initial fitness function is generated to measure the quality of the current parameter set. The smaller its value, the better the optimization effect of the signal. Based on the generated initial fitness function, gradient descent processing is performed on the waveform equation parameters 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 filtered signal. After obtaining the updated filtered signal, the previous steps are repeated. The difference between the updated filtered signal and the digital audio data is calculated to obtain an updated residual signal, and the sum of squares of it is calculated to obtain an updated sum of squared residuals. The updated sum of squared residuals reflects the optimization effect after gradient adjustment. If the value of this sum of squared residuals is less than a preset threshold, it indicates that the current parameter set has been optimized enough, and the optimization process can be terminated, and the current parameter set is determined as the optimal waveform equation parameters. If the updated sum of squared residuals is still greater than or equal to the preset threshold, it means that the optimization is not completed and iteration needs to continue. 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 further optimization cannot be achieved within the specified number of times, and the system will terminate the iteration and output the current parameter set as the optimal waveform equation parameters. Through this cyclic iterative optimization process, the optimal waveform equation parameters are gradually approximated to achieve the purpose of improving the quality of audio transmission and reading.

[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, calculate the parameters of the harmonic suppression filter. The main function of the harmonic suppression filter is to reduce or eliminate harmonic distortion in the audio signal. Apply the harmonic suppression filter to filter the digital audio data to obtain the audio signal after harmonic suppression, improving the clarity and accuracy of the audio. Based on the background noise characteristics in the optimal waveform equation parameters, calculate the parameters of the adaptive noise canceller. Through the adaptive noise canceller, dynamically adjust the performance of the filter according to the noise characteristics, effectively removing noise without damaging the audio signal. Based on the parameters of the adaptive noise canceller, perform noise cancellation on the audio signal after harmonic suppression, reducing the ambient noise or background interference in the audio signal to obtain the audio signal after noise cancellation. Based on the dynamic range information in the optimal waveform equation parameters, calculate the parameters of the dynamic range adjuster to ensure that different volume parts of the audio signal have appropriate loudness during playback, avoiding problems such as overly loud or overly soft volumes. Through the dynamic range adjuster, boost the lower volume parts of the audio signal while controlling the peaks of the higher volume parts, ensuring that the audio signal has appropriate dynamic performance across the entire frequency range. Based on these parameters, perform dynamic range adjustment on the audio signal after noise cancellation to obtain the audio signal after dynamic range adjustment, making it have balanced dynamic characteristics. Generate a phase compensation lookup table based on the phase information in the optimal waveform equation. Phase distortion affects 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, ensure that each frequency component of the audio signal maintains the correct time alignment and restore the original phase characteristics of the signal. Perform frequency response analysis on the audio signal after phase correction to obtain the frequency response curve of the signal. The frequency response curve is an indicator for measuring the energy distribution of the audio signal at different frequencies, capable of reflecting the frequency characteristics in the signal and which frequency bands need to be adjusted. Based on the frequency response curve, calculate the corresponding equalizer parameters. The function of the equalizer is to adjust different frequency bands to achieve a balanced sound quality performance. Based on the equalizer parameters, perform equalization processing on the audio signal after phase correction to ensure that the audio signal maintains a uniform energy distribution across all frequency ranges, obtaining the optimized audio signal.

[0038] 104. Divide the optimized audio signal 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 scheme;

[0039] Specifically, the optimized audio signal is divided into time windows, and the audio signal is divided into multiple audio data segments of a fixed duration. Frame synchronization processing is performed on each audio data segment 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 start position of each data packet, making the entire audio signal transmission and reception process more orderly, and obtaining multiple audio data packets. Priority is assigned to the generated audio data packets to obtain the priority value of each audio data packet. The assignment is based on various factors such as the content importance, time sensitivity, and network conditions of each data packet to ensure that critical audio data packets are preferentially transmitted during network transmission, 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 transmission optimization model for wireless audio with time window constraints is constructed. The optimization model takes into account the real-time requirements of transmission and ensures the stable transmission of the audio signal in a wireless environment by combining conditions such as the network bandwidth and delay. The compact transmission sequence algorithm is applied to the wireless audio transmission optimization model for solution to obtain an initial transmission sequence. Based on the initial transmission sequence, cyclic linear swapping is performed on the audio data packets to generate multiple different candidate transmission sequences. Cyclic linear swapping is an effective optimization strategy to find potential better transmission schemes through different data packet arrangements. The candidate transmission sequences are evaluated, and the evaluation criteria usually include transmission delay, packet loss rate, bandwidth utilization, etc. Through the evaluation of the candidate transmission sequences, an optimal transmission sequence is determined. Based on the evaluated optimal transmission sequence, a corresponding transmission time window is assigned to each audio data packet so that the audio data packets are transmitted at the most appropriate time points to minimize the delay and packet loss problems, obtaining a transmission scheduling scheme.

[0040] Normalize the priority values of audio data packets, convert the priority values of each audio data packet into a unified scale to obtain normalized priority values. Parameterize the network transmission conditions. The network transmission conditions include multiple aspects such as bandwidth, latency, and packet loss rate. Through parameterization, these conditions are quantified into specific network parameters. Based on the normalized priority values and network parameters, construct an objective function to minimize the weighted sum of the total transmission time and the packet loss rate, that is, while ensuring as low a transmission time as possible, minimize the packet loss. Constrain the transmission time of audio data packets to obtain time window constraint conditions. By setting the earliest and latest transmission times of data packets, ensure that each data packet can be transmitted within the specified time range. Combine the objective function and the time window constraint conditions to obtain an optimization model for wireless audio transmission. Perform a linear programming transformation on the optimization model for wireless audio transmission to obtain a linear programming model. Based on the linear programming model, construct a compact transmission sequence algorithm. This algorithm includes three main sub-processes: initialization, sequence generation, and optimization. In the initialization stage, set relevant parameters. In the sequence generation sub-process, sort the audio data packets according to the earliest possible transmission time to obtain an initial transmission sequence. Based on the initial sequence, apply the sequence generation sub-process of the compact transmission sequence algorithm to generate multiple candidate transmission sequences. The process of sequence generation searches for possible optimization solutions by continuously permuting and combining the transmission orders of data packets to form a set of candidate transmission sequences. Apply the optimization sub-process of the compact transmission sequence algorithm to the set of candidate transmission sequences to optimize the candidate sequences. The optimization sub-process selects the optimal transmission order through a weighted comprehensive evaluation of the transmission time and the packet loss rate, and makes adjustments on this basis until the optimal balance between the transmission time and the packet loss rate is achieved. Obtain the initial transmission sequence.

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

[0042] Specifically, perform content analysis on multiple audio data packets to obtain corresponding audio type information, which includes the type, compression ratio, importance, etc. of the audio. Based on the audio type information, select an appropriate encoding algorithm for each audio data packet. For example, for important audio parts, select an efficient and reliable encoding algorithm, while for relatively less important parts, select an algorithm with a higher compression ratio. Through selection, construct a set of encoding algorithms, which contains different encoding schemes for different audio types. Encode multiple audio data packets based on the set of encoding algorithms to convert the original audio data packets into an encoded audio stream. Perform hierarchical processing on the encoded audio stream to obtain base layer and enhancement layer audio data. The base layer audio data usually contains the core information of the audio, ensuring that even if data loss occurs during transmission, the audio in the base layer can be completely received and played. The enhancement layer audio data, on the other hand, 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, modulate the base layer audio data to convert the base layer audio data into base 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. Segment the enhancement layer audio data, divide the enhancement layer data into multiple small segments for more flexible transmission scheduling and resource allocation. Based on the transmission scheduling scheme, modulate multiple enhancement layer data segments to obtain multiple enhancement layer OFDM symbols. The enhancement layer OFDM symbols can be selectively transmitted according to network conditions during transmission, ensuring that the base layer is preferentially transmitted when the bandwidth is limited, and more enhancement layer symbols are transmitted when the bandwidth is sufficient to improve the sound quality. Perform power allocation on the base layer OFDM symbols and multiple enhancement layer OFDM symbols, reasonably allocate the energy resources in wireless transmission, ensure that the base layer audio symbols obtain sufficient power to ensure their reliable transmission, and the enhancement layer symbols can allocate the remaining power according to the transmission conditions. Through this allocation method, the overall efficiency of audio transmission can be improved and the error rate during transmission can be reduced. Based on the power allocation scheme, perform a merging process on the base layer OFDM symbols and multiple enhancement layer OFDM symbols 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 the received signal, perform decoding processing on the received signal to obtain the decoded audio data, and perform recombination on the decoded audio data to obtain the reconstructed digital audio signal.

[0044] Specifically, perform cyclic prefix removal on the received multi-layer OFDM symbols to obtain a pure OFDM symbol sequence. Perform transformation processing on the pure OFDM symbol sequence to convert it from the time domain to the frequency domain, obtaining a frequency domain signal that contains the frequency information of multi-layer audio data. Based on a preset power allocation scheme, perform layer demodulation on the frequency domain signal to obtain a base layer signal and multiple enhancement layer signals, ensuring that data with different priorities can be correctly processed during wireless transmission. Perform channel equalization on the base layer signal and multiple enhancement layer signals respectively. By compensating for the interference and noise effects of the channel during transmission, correct the signal distortion to obtain an equalized multi-layer signal. Based on a preset set of coding algorithms, perform decoding on the equalized multi-layer signal. Since signals at different levels may use different coding algorithms during transmission, during decoding, decode the signals of the base layer and enhancement layer respectively according to the corresponding algorithms in the set of coding algorithms. Through decoding, the original base layer audio data and enhancement layer audio data are restored. Merge the decoded base layer audio data and enhancement layer audio data to obtain the complete decoded audio data. Perform packet recombination on the decoded complete audio data, recombine the audio data packets divided during transmission into a continuous digital audio data stream, restore the time and content order of the original audio signal, and ensure that the data that was not lost or scrambled during transmission can be rearranged into the correct format. Perform digital-to-analog conversion on the recombined digital audio data stream to convert the digital-format audio data back into an analog signal for playback through devices such as speakers. Through digital-to-analog conversion, finally obtain the reconstructed analog audio signal.

[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 performing step 101 may specifically include the following steps:

[0048] Sample the original audio signal to obtain a discrete-time signal, and perform quantization processing on the discrete-time signal to obtain digital audio data;

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

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

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

[0052] Perform parameterization processing on the fundamental frequency component to obtain parameters representing the fundamental frequency amplitude, angular frequency, and phase; perform parameterization processing on the harmonic component to obtain parameters representing the amplitudes and phases of each harmonic; perform parameterization processing on the DC bias to obtain a parameter representing the bias value;

[0053] Combine the parameters representing the fundamental frequency amplitude, angular frequency, and phase, the parameters representing the amplitudes and phases of each harmonic, and the parameter representing the bias value to obtain a waveform equation containing multiple undetermined parameters, 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 amplitudes and phases 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 obtains the instantaneous values of the audio signal at fixed time intervals, converting it from the continuous domain to the discrete-time domain. 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 with a highest frequency of 20 kHz, the sampling frequency should be 40 kHz or higher. The discrete-time signal is quantized to discretize the continuous amplitude values of the discrete-time signal, converting the voltage values at the sampling points into a series of discrete digital values. The quantization precision 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 quantization precision, and the dynamic range and sound quality of the audio signal also improve. Through quantization processing, the discrete-time signal is converted into digital audio data. The digital audio data is subjected to a fast Fourier transform 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. The complex time-domain signal is decomposed into the superposition of several sine waves through the fast Fourier transform, and each sine wave has a specific frequency, amplitude, and phase. The frequency-domain signal is subjected to spectral analysis. Spectral analysis is used to study the frequency characteristics of the signal, and it can display the amplitude distribution of different frequency components. Through the spectrogram, the frequency energy distribution of the audio signal is reflected. Through spectral analysis, the energy distribution of different frequencies in the signal is identified, helping to identify the error sources affecting the audio quality. These errors may come from noise, harmonic distortion, or interference during transmission. Based on the spectral analysis results, error identification is carried out to find the abnormal frequency components or distortion components in the signal and determine the target error sources affecting the audio quality. For example, if some frequency components that should not exist or excessive harmonic components are found in the spectrum, then these may be caused by distortion. Through the analysis of the errors, the specific error types and sources are determined. Based on the error sources, the basic structure of the waveform equation of the audio signal is determined. The basic structure of the waveform equation usually includes three parts: the fundamental frequency component, the harmonic component, and the DC bias. The fundamental frequency component represents the main frequency of the signal, that is, the main component of the audio signal, usually the core part of the pitch. The harmonic component is an integer multiple of the fundamental frequency, reflecting the timbre characteristics of the audio signal. The DC bias is a constant term in the signal, representing the average value offset of the signal, and usually appears as an overall rise or fall of the signal in the actual signal. The fundamental frequency component is parameterized. The amplitude A of the fundamental frequency represents the intensity of the signal, and the angular frequency ω determines the frequency of the signal. 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 in the form of a sine wave. Similarly, the harmonic components are parameterized. Each harmonic component can be represented as amplitude B n and phase φ nThe sine wave. The frequencies of the harmonics are integer multiples of the fundamental frequency, so their angular frequencies are nω. The combination of these harmonics determines the complexity and timbre characteristics of the audio signal. The DC bias C is a constant representing the overall offset of the signal. These parameters are combined to form a complete waveform equation that can describe the frequency, amplitude, phase, and bias of the audio signal.

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

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

[0059] Calculate the difference between the initial filtered signal and the digital audio data to obtain an initial residual signal, and calculate the sum of squares of the initial residual signal to obtain an initial sum of squares of residuals;

[0060] Take the initial sum of squares of residuals as the initial value of the fitness function and generate an initial fitness function. Based on the initial fitness function, perform gradient descent processing on the waveform equation parameters to obtain an updated parameter set;

[0061] 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 perform filtering processing on the digital audio data to obtain an updated filtered signal;

[0062] Calculate the difference between the updated filtered signal and the digital audio data to obtain an updated residual signal, and calculate the sum of squares of the updated residual signal to obtain an updated sum of squares of residuals;

[0063] Compare the updated sum of squares of residuals with a preset threshold. If it is less than the preset threshold, determine the current parameter set as the optimal waveform equation parameters;

[0064] If it is greater than or equal to the preset threshold, continue the iteration, and perform counting processing on the number of iterations to obtain an iteration count value. Compare the iteration count value with the preset maximum number of iterations. If the preset maximum number of iterations is reached, terminate the iteration and output the current parameter set as the optimal waveform equation parameters.

[0065] Specifically, the undetermined parameters in the waveform equation are randomly initialized to obtain an initial parameter set, providing 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 difference between the initial filtered signal and the original digital audio data is calculated. The difference reflects the error between the filtered signal and the original signal, i.e., the initial residual signal. The residual signal represents the deviation degree between the filtered signal and the original signal. To quantify the error, the sum of squares of the initial residual signal is calculated to obtain the initial sum of squared residuals. The sum of squared residuals R is one of the criteria for measuring the filtering effect and can be expressed by the following formula:

[0066]

[0067] where 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 samples of the data. The initial sum of squared residuals 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, and the goal is to minimize the value of the fitness function to 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 with respect to each parameter and then updates the parameters along the opposite direction of the gradient to gradually approach the optimal solution. The update of the parameter can be expressed as:

[0068]

[0069] where θ old is the current parameter, η is the learning rate, representing the step size of parameter update, It 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 to make the filter better match the input audio signal. The coefficients of the adaptive filter are updated based on the updated parameter set to generate a new adaptive filter. This filter filters the digital audio data using the new parameter set to produce an updated filtered signal, thereby detecting whether the parameter update has reduced the error, that is, whether the deviation between the filtered signal and the original audio signal has decreased. The updated filtered signal is again differenced from the original audio data to obtain an updated residual signal. The sum of squares of the updated residual signal is calculated 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 indicates that the filtering effect is already good enough and the parameter set has converged to the ideal value. At this time, the current parameter set is determined as the optimal waveform equation parameter. If the updated residual sum of squares is greater than or equal to the preset threshold, then iterative optimization needs to continue. During each iteration, the number of iterations is counted to record the number of optimization rounds that have been performed. To prevent getting into an infinite loop or exceeding a reasonable calculation time, the iteration count value is compared with a preset maximum number of iterations. If the number of iterations has reached the preset maximum value, it is considered that the current parameter set is already the best solution or close to the best solution. Although its residual sum of squares may not fully reach the preset threshold, the system terminates the iteration at this time and outputs the current parameter set as the optimal waveform equation parameter.

[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 parameter, calculate the harmonic suppression filter parameter, and filter the digital audio data based on the harmonic suppression filter parameter to obtain a harmonically suppressed audio signal;

[0072] Based on the background noise characteristics in the optimal waveform equation parameter, calculate the adaptive noise canceller parameter, and cancel the noise of the harmonically suppressed audio signal based on the adaptive noise canceller parameter to obtain a noise-cancelled audio signal;

[0073] Based on the dynamic range information in the optimal waveform equation parameter, calculate the dynamic range adjuster parameter, and adjust the dynamic range of the noise-cancelled audio signal based on the dynamic range adjuster parameter to obtain a dynamically range-adjusted audio signal;

[0074] Based on the phase information in the optimal waveform equation parameter, generate a phase compensation look-up table, and perform phase correction on the dynamically range-adjusted audio signal based on the phase compensation look-up table to obtain a phase-corrected audio signal;

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

[0076] Based on the equalizer parameters, perform equalization processing on the phase-corrected audio signal to obtain an optimized audio signal.

[0077] Specifically, analyze the harmonic components. Harmonic components are integer multiples of the fundamental frequency. In an audio signal, these harmonics often cause distortion, especially when the unnecessary higher-order harmonics are strong. Based on the harmonic components in the optimal waveform equation, calculate the filter parameters for suppressing unnecessary harmonics. For example, let the fundamental frequency be f0, and the angular frequency be ω0 = 2πf0. Suppose there are some higher-order harmonics B n and φ n that have a negative impact on the sound quality. The notch filter can reduce the amplitude of unnecessary harmonics in the frequency domain. The design of the filter is based on the frequency position and amplitude of the harmonics, and usually a band-stop filter (also known as a notch filter) is used to suppress specific harmonic frequency bands. The transfer function of the band-stop filter can be expressed as:

[0078]

[0079] where f n represents the harmonic frequency to be suppressed. By filtering the audio data using this filter, the interference of harmonics on the sound quality is effectively reduced, and an audio signal with harmonic suppression is obtained. Based on the background noise characteristics in the optimal waveform equation parameters, calculate the adaptive noise canceller parameters. The adaptive noise canceller is a filter used to remove noise, and it dynamically adjusts the filter coefficients according to the time or frequency changes of the signal. Using an adaptive filtering algorithm such as the LMS (Least Mean Square) algorithm, the filter parameters are dynamically adjusted 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, apply the adaptive noise canceller to the audio signal with harmonic suppression to obtain an audio signal with noise cancellation. According to the dynamic range characteristics of the audio signal, calculate the parameters of the dynamic range adjuster. The dynamic range adjuster is used to balance different volume parts of the audio signal, ensuring that the weak sound parts can be enhanced while the strong sound parts do not exceed the normal range. This process is usually achieved through a compressor or an expander. The compressor controls the signal peak by reducing the gain of the high-volume signal. The formula is:

[0082]

[0083] Among them, 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 enhance the weaker signals while controlling the stronger signals to avoid clipping distortion, and obtain the audio signal with adjusted dynamic range. 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 has an offset, it can be corrected by the corresponding compensation value in the lookup table to restore the phase to the ideal state. Using this lookup table to perform phase correction on the audio signal with adjusted dynamic range 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 enhanced or weakened. Usually, uneven frequency response will lead to deviations in sound quality, such as too strong low frequency or unclear high frequency. Based on the results of the frequency response analysis, calculate the equalizer parameters. The equalizer optimizes the frequency distribution of the audio signal by adjusting the gain 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) represent the gains of low frequency, medium frequency, and high frequency respectively. By adjusting these gains, make the frequency response curve flatter and achieve frequency equalization processing. Based on the equalizer parameters, apply the equalizer to perform equalization processing on the phase-corrected audio signal, and finally obtain the optimized audio signal.

[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 a fixed duration, and perform frame synchronization and add frame headers to each audio data segment to obtain multiple audio data packets;

[0088] Assign priorities to the multiple audio data packets to obtain the priority values of each audio data packet;

[0089] Based on the priority values of the audio data packets and the network transmission conditions, construct a transmission optimization model for wireless audio with time window constraints, and apply the compact transmission sequence algorithm to process the wireless audio transmission optimization model to obtain an initial transmission sequence;

[0090] Based on the initial transmission sequence, perform cyclic linear swapping on the audio data packets to obtain multiple candidate transmission sequences;

[0091] Evaluate the multiple candidate transmission sequences to obtain the optimal transmission sequence, and based on the optimal transmission sequence, allocate transmission time windows to each audio data packet to obtain a transmission scheduling scheme.

[0092] Specifically, divide the optimized audio signal into time windows, and divide the continuous audio signal into several smaller audio data segments at a fixed duration. Perform frame synchronization and add frame headers to 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 a synchronization identifier, packet length, and timestamp, enabling the receiving end to 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, assign priorities to the multiple audio data packets to obtain the priority value of each audio data packet. For example, the critical part of a voice conversation may be assigned a higher priority to ensure its priority transmission, while the secondary background sound may be assigned a lower priority. Construct an optimization model for wireless audio transmission with time window constraints based on the priority value of the audio data packet and network transmission conditions, thereby optimizing 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, and 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 the audio data packet while maximizing the utilization rate of network resources. To solve the transmission optimization model, use the compact transmission sequence algorithm. 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 data packet and network conditions. Mathematically, the transmission sequence can be represented by T s and the optimization objective of the initial transmission sequence is:

[0093]

[0094] where P i represents the priority value of the i-th data packet, D iLet \(T_i\) denote the transmission delay of the \(i\)-th data packet, and \(N\) denote the total number of data packets. The optimization objective is to minimize the weighted sum of priority and delay, ensuring that high-priority data packets are transmitted with the lowest delay. Based on the initial transmission sequence, cyclic linear swapping is performed on the audio data packets. Cyclic linear swapping is a strategy for optimizing the transmission order. By different permutations and swapping of the transmission order, better candidate transmission sequences are searched. By swapping 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, packet loss rate, and network resource utilization of each sequence. Through comparing the performance of different candidate transmission sequences in the evaluation process, the sequence with the best performance is selected. Based on the optimal transmission sequence, a transmission time window is allocated to each audio data packet, enabling each packet to be transmitted within 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 audio under limited bandwidth and complex network conditions.

[0095] In a specific embodiment, the process of constructing an optimization model for wireless audio transmission with time window constraints based on the priority value of audio data packets and network transmission conditions, and applying a compact transmission sequence algorithm to the wireless audio transmission optimization model to obtain an initial transmission sequence can specifically include the following steps:

[0096] Normalize the priority value of the audio data packets to obtain a normalized priority value, and parameterize the network transmission conditions to obtain network parameters;

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

[0098] Perform constraint processing on the transmission time of the audio data packets to obtain time window constraint conditions, and combine the objective function and time window constraint conditions to obtain an optimization model for wireless audio transmission;

[0099] Perform linear programming transformation on the optimization model for wireless audio transmission to obtain a linear programming model, and based on the linear programming model, construct a compact transmission sequence algorithm, where the compact transmission sequence algorithm includes three sub-processes: initialization, sequence generation, and optimization;

[0100] Sort the audio data packets according to the earliest possible transmission time to obtain an initial sequence, and based on the initial sequence, apply the sequence generation sub-process of the compact transmission sequence algorithm to obtain a set of candidate transmission sequences;

[0101] Apply the optimization sub-process of the compact transmission sequence algorithm to the set of candidate transmission sequences to obtain an initial transmission sequence.

[0102] Specifically, normalize the priority values of each audio data packet, converting the priority values of different data packets into a standardized range, usually between [0, 1], to obtain the normalized priority values. Parameterize the network transmission conditions. The network transmission conditions include multiple parameters such as bandwidth, latency, and data packet loss rate. Assume the network bandwidth is B, the latency 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 values and network parameters, construct an objective function to minimize the weighted sum of the total transmission time and the data packet loss rate, ensuring that important data packets can be transmitted quickly during the transmission process while effectively reducing the risk of data packet loss. The objective function can be expressed as:

[0103]

[0104] where T i represents the transmission time of the i-th data packet, L i represents the packet loss rate of the i-th data packet, w1 and w2 are the weight coefficients of the transmission time and the 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. To improve the practicality of the model, constraint processing is performed on the transmission time of the audio data packet to obtain the time window constraint condition. The time window constraint condition 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] where T max is the maximum allowed transmission time window. Combine the objective function with the time window constraint condition to obtain the transmission optimization model for wireless audio. This model comprehensively considers the transmission time, packet loss rate, and time window constraint to ensure that while meeting the real-time transmission requirements, the loss and delay of data packets are minimized as much as possible. To solve this transmission optimization model, perform a linear programming transformation on it. Linear programming is a commonly used optimization method that can convert a non-linear problem into a linear form for more efficient solution. The transformed linear programming model can be expressed as:

[0107]

[0108] where x i is a decision variable representing whether to select data packet i for transmission. x i = 1 indicates selection for transmission, x i= 0 means no transmission. After conversion by linear programming, a 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, initial transmission parameters and network conditions are set. The sequence generation sub - process generates an initial transmission sequence according to the normalized priority values and network conditions, ensuring 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 reduction of transmission delay and packet loss rate. The audio data packets are sorted according to the earliest possible transmission time to obtain an initial sequence. The sorting of the initial sequence is based on the priority values and network conditions of each data packet. Data packets with high priority and early transmission time are arranged at the front of the sequence. Suppose the priorities of the data packets in the initial sequence are P1, P2, …, P N , through the sequence generation sub - process of the compact transmission sequence algorithm, a set of transmission sequences is initially generated. After generating the initial sequence, the optimization sub - process of the algorithm adjusts the sequence. By cyclically swapping the positions of data packets, the optimization sub - process 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 can be transmitted with the lowest possible delay under the premise of meeting the time - window constraints. The set of candidate transmission sequences is evaluated, and by comparing the performance of different sequences, the optimal transmission sequence is selected. The evaluation criteria usually include total transmission time, packet loss rate, and bandwidth utilization, etc. Through the analysis of these criteria, it is determined which sequence has the optimal transmission performance under the current network conditions. Based on the optimal transmission sequence obtained from the evaluation, a transmission time window is assigned 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] Perform content analysis on multiple audio data packets to obtain audio type information, and based on the audio type information, select corresponding coding algorithms for each audio data packet to obtain a set of coding algorithms;

[0111] Encode multiple audio data packets based on the set of coding algorithms to obtain an encoded audio stream, and layer the encoded audio stream to obtain base - layer and enhancement - layer audio data;

[0112] Modulate the base - layer audio data based on the transmission scheduling scheme to obtain base - layer OFDM symbols;

[0113] Segment the enhancement - layer audio data to obtain multiple enhancement - layer data segments, and based on the transmission scheduling scheme, modulate the multiple enhancement - layer data segments to obtain multiple enhancement - layer OFDM symbols;

[0114] Perform power allocation on the basic layer OFDM symbol and multiple enhanced layer OFDM symbols to obtain a power allocation scheme, and based on the power allocation scheme, combine the basic layer OFDM symbol and multiple enhanced layer OFDM symbols to obtain a multi-layer OFDM symbol.

[0115] Specifically, perform content analysis on multiple audio data packets to obtain the audio type information of each audio data packet. The audio type information refers to the nature of the audio, such as speech, music, background sound, etc. For example, a speech signal usually contains low-frequency and mid-frequency components and has a high requirement for clarity, while a music signal contains rich frequency information and emphasizes high-fidelity and dynamic range. Select a corresponding coding algorithm for each audio data packet based on the audio type information. Different audio types are suitable for different coding methods. For example, a speech signal can select a coding algorithm with a higher compression rate and retain clarity, such as adaptive multi-rate codec, while a music signal can select a coding algorithm that retains more frequency information, such as advanced audio coding. These coding algorithms can retain the core information of the audio with as little data volume as possible. By analyzing the type of each audio data packet and selectively choosing a coding algorithm, a set of coding algorithms is obtained. Based on the set of coding algorithms, encode multiple audio data packets to obtain an encoded audio stream. The encoding process compresses and processes the original audio data so that the audio data can be efficiently transmitted over the network. Perform hierarchical processing on the encoded audio stream to optimize the transmission of audio data with different priorities. The audio stream is usually divided into two layers: the basic layer and the enhanced layer. The basic layer contains the core information of the audio signal, and the enhanced layer contains more sound quality details. Based on the transmission scheduling scheme, modulate the basic layer audio data to obtain the basic 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 the data into multiple sub-carriers for transmission. The generation of the OFDM symbol can be achieved by modulating the audio data with QAM (Quadrature Amplitude Modulation) or PSK (Phase Shift Keying). Assuming QAM is used for modulation, the basic layer OFDM symbol can be expressed as:

[0116]

[0117] where, X k is the basic layer OFDM symbol, A k is the amplitude, representing the magnitude of the signal, θ kis the phase, representing 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 into multiple enhanced layer data segments, and each enhanced layer data segment contains partial 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. Similar to the base layer symbols, the enhanced layer OFDM symbols can achieve efficient data transmission through OFDM modulation. Power allocation is performed on the base layer OFDM symbols and multiple enhanced layer OFDM symbols to ensure that under the limited total transmission power, the transmission quality of the base layer symbols is preferentially guaranteed, and reasonable remaining power is allocated to the enhanced layer symbols. The power allocation scheme can be expressed by the following formula:

[0118]

[0119] where P total is the total power, P basic is the power allocated to the base layer OFDM symbol, and P enhance,n is the power of the nth enhanced layer OFDM symbol. By reasonably allocating these powers, it is ensured that the base layer obtains sufficient energy for transmission, and the enhanced layer dynamically adjusts the power according to the network situation. Based on the power allocation scheme, the base layer OFDM symbols and multiple enhanced 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] Remove the cyclic prefix from the received multi-layer OFDM symbols to obtain a pure OFDM symbol sequence, and perform a transformation on the pure OFDM symbol sequence to obtain a frequency-domain signal;

[0122] Based on the preset power allocation scheme, perform hierarchical demodulation on the frequency-domain signal to obtain the base layer signal and multiple enhanced layer signals, and perform channel equalization on the base layer signal and multiple enhanced layer signals respectively to obtain the equalized multi-layer signals;

[0123] Based on the preset set of coding algorithms, decode the equalized multi-layer signals to obtain the decoded base layer audio data and enhanced layer audio data;

[0124] Merge the decoded base layer audio data and enhanced layer audio data to obtain the complete decoded audio data;

[0125] Perform packet recombination on the complete decoded audio data to obtain the reconstructed digital audio data stream, and perform digital-to-analog conversion on the reconstructed digital audio data stream to obtain the reconstructed analog audio signal.

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

[0127]

[0128] where X k is the k-th frequency-domain signal, x n is the n-th sample in the time domain, and N is the length of the fast Fourier transform. After the fast Fourier transform, the receiving end can analyze the amplitude and phase of each frequency component to obtain the frequency-domain representation of the OFDM symbol. Based on a preset power allocation scheme, the frequency-domain signal is demodulated in layers to restore the received signal into audio data of different layers. The demodulation process uses the preset power allocation scheme to separate the frequency-domain signal according to the power distribution of different-layer signals, obtaining the base-layer signal and multiple enhancement-layer signals. Channel equalization processing is performed on the base-layer signal and the enhancement-layer signals respectively to compensate for the distortion of the signal caused by factors such as channel fading and interference during wireless transmission. Common channel equalization methods include minimum mean square error equalization and zero-forcing equalization. Channel equalization uses the channel state information at the receiving end 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] where is the equalized signal, X k is the received frequency-domain signal, and 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 the 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 the 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 subjected to packet recombination, and the scattered packets are recombined into a continuous audio data stream to obtain a reconstructed digital audio data stream. By checking the timestamps and sequence numbers of each packet, it is ensured that all packets are correctly sorted to avoid data loss or overlap. The reconstructed digital audio data stream is subjected to digital-to-analog conversion. The digital-to-analog converter converts the digital audio data stream into an analog signal to restore the original waveform and obtain a reconstructed analog audio signal.

[0131] In a specific embodiment, the process of performing step of performing hierarchical 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 respectively performing channel equalization on the base layer signal and the multiple enhancement layer signals to obtain an equalized multi-layer signal may specifically include the following steps:

[0132] Perform power spectrum analysis on the frequency-domain signal to obtain a signal power distribution map, and based on the signal power distribution map, perform subcarrier allocation on the frequency-domain signal to obtain a base layer subcarrier set and multiple enhancement layer subcarrier sets;

[0133] Demodulate the base layer subcarrier set to obtain a base layer signal, and respectively demodulate the multiple enhancement layer subcarrier sets to obtain multiple enhancement layer signals;

[0134] 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;

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

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

[0137] Combine the equalized base layer signal and the multiple equalized enhancement layer signals to obtain an equalized multi-layer signal.

[0138] Specifically, perform power spectrum analysis on the frequency-domain signal. Power spectrum analysis is a frequency-domain analysis technique that can reveal the energy distribution of each frequency component in the signal. By performing power spectrum analysis, understand the power density of each frequency, that is, the intensity of the signal at different frequencies. Suppose there is a frequency-domain signal X(f) after Fourier transform, and 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 corresponding to frequency f. By calculating the power spectrum, the power distribution map of the signal is plotted, showing the energy distribution of the signal at different frequencies. Based on the signal power distribution map, subcarrier allocation is performed on the frequency-domain signal, and the frequency-domain signal is allocated to different layers to meet the requirements of multi-layer transmission. Usually, frequencies with stronger power will be preferentially allocated to the base-layer signal because the base-layer signal undertakes the transmission task of core information and requires higher reliability. Frequencies with weaker power can be allocated to the enhancement-layer signals, which are used to improve the sound quality and have higher fault tolerance. Through the subcarrier allocation process, the frequency range is divided into a base-layer subcarrier set and multiple enhancement-layer subcarrier sets. Demodulation is performed on the base-layer subcarrier set to obtain the base-layer signal. Demodulation is the process of restoring the modulated OFDM symbols to the original digital data. During the demodulation process, the receiving end uses the received subcarrier data and converts the frequency-domain signal back to the time-domain signal through methods such as inverse Fourier transform. For the enhancement-layer subcarrier sets, demodulation is performed on each of them to obtain multiple enhancement-layer signals. These enhancement-layer signals respectively 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 includes information such as the fading characteristics, noise, and interference of the channel, reflecting the changes of the signal during transmission. 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, channel equalization is performed on the base-layer signal 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 channel estimation result of the base layer, channel estimation is performed on each enhancement-layer signal. The enhancement-layer signals are affected by channel fading and noise during transmission and need channel estimation to analyze the transmission characteristics of the signals. Based on the channel estimation result of each enhancement layer, channel equalization is performed on each enhancement-layer signal. The equalized enhancement-layer signals restore more detail parts 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 higher sound quality performance under better network conditions. Through channel equalization, the distortion of the enhancement-layer signals is effectively compensated to ensure the integrity of the signals. The equalized base-layer signal and multiple enhancement-layer signals are combined to obtain the final multi-layer signal. The base-layer signal provides the core information of the audio, ensuring that important content can be transmitted even under poor network conditions; the enhancement-layer signals enrich the audio details, and under allowable network conditions, the enhancement-layer signals can provide users with a high-fidelity audio experience.

[0141] The wireless audio data transmission and reading methods in the embodiments of the present invention have been described above. Next, the audio playback device in the embodiments of the present invention will be described. Please refer to Figure 2 , one embodiment of the audio playback device in the embodiments of the present invention includes:

[0142] A signal processing module 201, configured to 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;

[0143] A parameter optimization module 202, configured 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;

[0144] A creation module 203, configured 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, configured to partition the optimized audio signal 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 scheme;

[0146] An encoding module 205, configured to perform encoding processing on the 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] A decoding module 206, configured to perform signal reconstruction processing on the multi-layer OFDM symbols to obtain a received signal, perform decoding processing on the received signal to obtain decoded audio data, and perform recombination on the decoded audio data to obtain a reconstructed digital audio signal.

[0148] Through the collaborative cooperation of the above-mentioned various components, by establishing an accurate waveform equation and optimizing parameters, this method can more accurately describe and process audio signals, effectively reduce signal distortion, and improve audio quality. By adopting a multi-level signal processor, including harmonic suppression, noise cancellation, dynamic range adjustment, etc., it can comprehensively optimize audio signals and significantly improve the sound quality. By introducing a transmission optimization model and a compact transmission sequence algorithm, it realizes a more efficient data packet transmission scheduling, reduces the transmission delay, and improves the network resource utilization rate. By adopting a multi-layer OFDM symbol mapping technology, it enhances the adaptability and anti-interference ability of the system in a complex network environment and improves the transmission reliability. Through hierarchical encoding and decoding technologies, it realizes the hierarchical transmission of audio data, ensures the basic sound quality under limited bandwidth conditions, and provides a higher-quality audio experience when conditions permit. The signal reconstruction and equalization processing technology at the receiving end effectively compensates for the channel distortion during transmission and improves the restoration degree 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 applicable to 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. Next, the electronic device in the embodiment of the present invention is described in detail from the perspective of hardware processing.

[0150] Figure 3 It is a schematic structural diagram of an electronic device provided by an embodiment of the present invention. The electronic device 300 may vary greatly due to configuration or performance differences and may include one or more processors (central processing units, CPUs) 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 ends) that store application programs 333 or data 332. Among them, the memory 320 and the storage media 330 can be short-term storage or persistent storage. The program stored in the storage media 330 may include one or more modules (not shown in the figure), and each module may include a series of instruction operations on the electronic device 300. Further, the processor 310 can be set to communicate with the storage media 330 and execute a series of instruction operations in the storage media 330 on the electronic device 300 to implement the steps of the above wireless audio data transmission and reading method.

[0151] The electronic device 300 may further include one or more power supplies 340, one or more wired or wireless network interfaces 350, one or more input / output interfaces 360, and / or one or more operating systems 331, such as Windows Serve, Mac OS X, Unix, Linux, FreeBSD, etc. Those skilled in the art can understand that Figure 3 The illustrated structure of the electronic device does not limit the electronic device provided by the present invention, and it may include more or fewer components than those illustrated, or combine certain components, or have different component arrangements.

[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 run on a computer, the computer is caused to execute 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 may refer to the corresponding processes in the foregoing method embodiments and will not be described herein again.

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

[0155] The above is the case. The above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments or perform equivalent replacements for some of the technical features. These modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A method for wireless audio data transmission and reading, characterized in that, The method includes: 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; Performing parameter optimization on the waveform equation 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 through the signal processor to obtain an optimized audio signal; Performing data partitioning on the optimized audio signal to obtain 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 scheme; Based on the transmission scheduling scheme, performing encoding processing on the multiple audio data packets to obtain an encoded audio stream, and performing multi-layer mapping on the encoded audio stream to obtain multi-layer OFDM symbols; Performing signal reconstruction processing on the multi-layer OFDM symbols to obtain a received signal, performing decoding processing on the received signal to obtain decoded audio data, and performing recombination on the decoded audio data to obtain a reconstructed digital audio signal.

2. The wireless audio data transmission and reading method according to claim 1, characterized in that The 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: Performing sampling processing on the original audio signal to obtain a discrete-time signal, and performing quantization processing on the discrete-time signal to obtain the digital audio data; Performing a fast Fourier transform on the digital audio data to obtain the frequency-domain signal; Performing spectral analysis on the frequency-domain signal to obtain the spectral characteristics of the signal, and performing error identification based on the spectral characteristics to obtain the target error sources affecting audio quality; Determining the basic structure of the waveform equation based on the target error sources, where the basic structure includes a fundamental frequency component, harmonic components, and a DC bias; Performing parameterization processing on the fundamental frequency component to obtain parameters representing the fundamental frequency amplitude, angular frequency, and phase; performing parameterization processing on the harmonic components to obtain parameters representing the amplitudes and phases of each harmonic; performing parameterization processing on the DC bias to obtain a parameter representing the bias value; Combining the parameters representing the fundamental frequency amplitude, angular frequency, and phase, the parameters representing the amplitudes and phases of each harmonic, and the parameter representing the bias value to obtain a waveform equation containing multiple undetermined parameters, where 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 respectively the amplitudes and phases of the harmonic components, and C is the DC bias.

3. The wireless audio data transmission and reading method according to claim 1, characterized in that Performing parameter optimization on the waveform equation 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, including: Performing random initialization processing on the undetermined parameters in the waveform equation to obtain an initial parameter set, and based on the initial parameter set, performing filtering processing on the digital audio data to obtain an initial filtered signal; Calculating the difference between the initial filtered signal and the digital audio data to obtain an initial residual signal, and calculating the sum of squares of the initial residual signal to obtain an initial sum of squares of residuals; Taking the initial sum of squares of residuals as the initial value of the fitness function and generating an initial fitness function, and based on the initial fitness function, performing gradient descent processing on the waveform equation parameters to obtain an updated parameter set; Updating the coefficients of the adaptive filter based on the updated parameter set to obtain an updated adaptive filter, and using the updated adaptive filter to perform filtering processing on the digital audio data to obtain an updated filtered signal; Calculating the difference between the updated filtered signal and the digital audio data to obtain an updated residual signal, and calculating the sum of squares of the updated residual signal to obtain an updated sum of squares of residuals; Comparing the updated sum of squares of residuals with a preset threshold, and if it is less than the preset threshold, determining the current parameter set as the optimal waveform equation parameters; If it is greater than or equal to the preset threshold, continue iterating, and performing counting processing on the number of iterations to obtain an iteration count value, comparing the iteration count value with a preset maximum number of iterations, and if the preset maximum number of iterations is reached, terminating the iteration and outputting the current parameter set as the optimal waveform equation parameters.

4. The wireless audio data transmission and reading method according to claim 1, wherein, Creating a signal processor based on the optimal waveform equation parameters, and processing the digital audio data through the signal processor to obtain an optimized audio signal, including: 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 the background noise characteristics in the optimal waveform equation parameters, and performing noise cancellation on the audio signal after harmonic suppression based on the adaptive noise canceller parameters to obtain an audio signal after noise cancellation; Calculating dynamic range adjuster parameters based on the dynamic range information in the optimal waveform equation parameters, and performing dynamic range adjustment on the audio signal after noise cancellation based on the dynamic range adjuster parameters to obtain an audio signal after dynamic range adjustment; Generating a phase compensation look-up table based on the phase information in the optimal waveform equation parameters, and performing phase correction on the audio signal after dynamic range adjustment based on the phase compensation look-up table to obtain an audio signal after phase correction; Perform frequency response analysis on the phase-corrected audio signal to obtain a frequency response curve, and calculate corresponding equalizer parameters based on the frequency response curve; Based on the equalizer parameters, perform equalization processing on the phase-corrected audio signal to obtain the optimized audio signal.

5. The wireless audio data transmission and reading method according to claim 1, characterized in that The data division of the optimized audio signal 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 scheme, including: Perform time window division on the optimized audio signal to obtain multiple audio data segments of a fixed duration, and perform frame synchronization and frame header addition on each audio data segment to obtain multiple audio data packets; Perform priority assignment on the multiple audio data packets to obtain the priority value of each audio data packet; Based on the priority value of the audio data packet and the network transmission condition, construct a transmission optimization model for wireless audio with time window constraints, and apply a compact transmission sequence algorithm to the wireless audio transmission optimization model to obtain an initial transmission sequence; Based on the initial transmission sequence, perform cyclic linear exchange on the audio data packets to obtain multiple candidate transmission sequences; Evaluate the multiple candidate transmission sequences to obtain an optimal transmission sequence, and based on the optimal transmission sequence, allocate a transmission time window to each audio data packet to obtain the transmission scheduling scheme.

6. The wireless audio data transmission and reading method according to claim 5, wherein The construction of a transmission optimization model for wireless audio with time window constraints based on the priority value of the audio data packet and the network transmission condition, and the application of a compact transmission sequence algorithm to the wireless audio transmission optimization model to obtain an initial transmission sequence, includes: Normalize the priority value of the audio data packet to obtain a normalized priority value, and parameterize the network transmission condition to obtain network parameters; Based on the normalized priority value and the network parameters, construct an objective function, where the objective function is to minimize the weighted sum of the total transmission time and the packet loss rate; Perform constraint processing on the transmission time of the audio data packet to obtain time window constraint conditions, and combine the objective function and the time window constraint conditions to obtain the transmission optimization model for wireless audio; Perform linear programming transformation on the transmission optimization model for wireless audio to obtain a linear programming model, and based on the linear programming model, construct a compact transmission sequence algorithm, where the compact transmission sequence algorithm includes three sub-processes: initialization, sequence generation, and optimization; Sort the audio data packets according to the earliest possible transmission time to obtain an initial sequence, and based on the initial sequence, apply the sequence generation sub-process of the compact transmission sequence algorithm to obtain a set of candidate transmission sequences; Apply the optimization sub-process of the compact transmission sequence algorithm to the set of candidate transmission sequences to obtain the initial transmission sequence.

7. The wireless audio data transmission and reading method according to claim 1, characterized in that, Based on the transmission scheduling scheme, perform encoding processing on the multiple audio data packets to obtain an encoded audio stream, and perform multi-layer mapping on the encoded audio stream to obtain multi-layer OFDM symbols, including: Perform content analysis on the multiple audio data packets to obtain audio type information, and based on the audio type information, select corresponding encoding algorithms for each audio data packet to obtain an encoding algorithm set; Encode the multiple audio data packets based on the encoding algorithm set to obtain an encoded audio stream, and layer the encoded audio stream to obtain base layer and enhancement layer audio data; Modulate the base layer audio data based on the transmission scheduling scheme to obtain base layer OFDM symbols; Segment the enhancement layer audio data to obtain multiple enhancement layer data segments, and modulate the multiple enhancement layer data segments based on the transmission scheduling scheme to obtain multiple enhancement layer OFDM symbols; Perform power allocation on the base layer OFDM symbols and the multiple enhancement layer OFDM symbols to obtain a power allocation scheme, and based on the power allocation scheme, combine the base layer OFDM symbols and the multiple enhancement layer OFDM symbols 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 process for the multi-layer OFDM symbols to obtain a received signal, the decoding process for the received signal to obtain decoded audio data, and the recombination of the decoded audio data to obtain a reconstructed digital audio signal include: Remove the cyclic prefix from the received multi-layer OFDM symbols to obtain a pure OFDM symbol sequence, and transform the pure OFDM symbol sequence to obtain a frequency domain signal; Based on a preset power allocation scheme, perform layered demodulation on the frequency domain signal to obtain a base layer signal and multiple enhancement layer signals, and perform channel equalization on the base layer signal and the multiple enhancement layer signals respectively to obtain equalized multi-layer signals; Decode the equalized multi-layer signals based on a preset encoding algorithm set to obtain decoded base layer audio data and enhancement layer audio data; Combine the decoded base layer audio data and enhancement layer audio data to obtain complete decoded audio data; Recombine the complete decoded audio data into data packets to obtain a reconstructed digital audio data stream, and perform digital-to-analog conversion on the reconstructed digital audio data stream to obtain a reconstructed analog audio signal.

9. The wireless audio data transmission and reading method according to claim 8, characterized in that, The 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: Perform power spectrum analysis on the frequency domain signal to obtain a signal power distribution map, and based on the signal power distribution map, perform subcarrier allocation on the frequency domain signal to obtain a base layer subcarrier set and multiple enhancement layer subcarrier sets; Demodulate the base layer subcarrier set to obtain a base layer signal, and demodulate the multiple enhancement layer subcarrier sets respectively to obtain multiple enhancement layer signals; Perform channel estimation on the base layer signal based on pre-estimated channel state information to obtain a base layer channel estimation result, and perform equalization on the base layer signal based on the base layer channel estimation result to obtain an equalized base layer signal; Perform channel estimation on the multiple enhancement layer signals respectively based on the pre-estimated channel state information and the base layer channel estimation result to obtain multiple enhancement layer channel estimation results; Perform equalization on the multiple enhancement layer signals respectively based on the multiple enhancement layer channel estimation results to obtain multiple equalized enhancement layer signals; Combine the equalized base layer signal and the multiple equalized enhancement layer signals to obtain the equalized multi-layer signal.

10. An audio playback device, characterized in that, For performing the wireless audio data transmission and reading method according to any one of claims 1-9, the device includes: A signal processing module, configured to 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; A parameter optimization module, configured 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, configured 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 partition the optimized audio signal 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 scheme; An encoding module, configured to perform encoding processing on the 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; A decoding module, configured to perform signal reconstruction processing on the multi-layer OFDM symbols to obtain a received signal, perform decoding processing on the received signal to obtain decoded audio data, and perform recombination on the decoded audio data to obtain a reconstructed digital audio signal.

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