Low-delay adaptive wireless audio transmission method

Abstract

The invention discloses a low-delay adaptive wireless audio transmission method, and relates to the technical field of audio data transmission. The invention relates to a low-delay self-adaptive wireless audio transmission method, which comprises the steps of audio signal acquisition and coding, priority scheduling and time slot allocation, wireless channel transmission and interference resistance, congestion control algorithm optimization, decoding and reconstruction of a receiving end, and dynamic buffer management and delay optimization. According to the method, low-delay transmission of audio signals is ensured through a plurality of technologies such as priority scheduling, time slot allocation, dynamic buffer management and adaptive coding, the stability of audio transmission is remarkably improved by combining congestion control, forward error correction (FEC) and adaptive interference elimination technologies, and even in a complex wireless environment, the stability of the audio transmission is greatly improved. And the audio signal can keep high quality, and the transmission stability is ensured by quickly responding to channel change, eliminating interference, correcting errors, reducing packet loss and retransmission.

Description

Technical Field

[0001] The present invention relates to the technical field of audio data transmission, and in particular to a low-delay adaptive wireless audio transmission method. Background Art

[0002] With the widespread use of wireless audio devices (such as wireless headphones, wireless microphones, smart speakers, conference systems, etc.), users have higher and higher requirements for the quality of audio transmission, especially in terms of sound quality, low latency and stability. However, there are some technical difficulties in audio transmission in wireless environments, such as multipath fading, channel interference, data packet loss and transmission delay, which will have a negative impact on the audio experience.

[0003] Traditional wireless audio transmission systems usually use fixed encoding methods and bandwidth allocation methods. This static design cannot adapt to dynamically changing wireless channel conditions, resulting in reduced transmission quality or increased latency when the channel environment is poor. In addition, conventional congestion control algorithms and scheduling mechanisms also make it difficult to ensure low latency and high stability of audio in real-time transmission. Most existing wireless audio transmission technologies will cause audio signal interruption, reduced sound quality or high latency when encountering network congestion or signal interference, thus affecting the user experience. Fixed buffer mechanisms and encoding schemes lack sufficient flexibility to deal with these dynamic problems.

[0004] When the wireless channel conditions are unstable, there is an urgent need for an audio transmission method that can automatically adjust according to the real-time channel status, and ensure the stability and low latency of audio transmission by adaptively adjusting the coding bit rate, anti-interference mechanism, congestion control strategy, etc. Summary of the invention

[0005] A low-delay adaptive wireless audio transmission method, characterized by comprising the following steps:

[0006] Step S1: audio signal acquisition and encoding, the audio signal is acquired, and an adaptive encoding strategy is used to compress and encode the audio signal based on the channel condition, wherein the encoding parameters are dynamically adjusted according to the channel condition to ensure the sound quality and transmission stability;

[0007] Step S2: Priority scheduling and time slot allocation: Priority scheduling is performed based on the media access control (MAC) layer. The time slot resources are adjusted in real time according to the service quality requirements of the data packet through a dynamic time slot allocation algorithm to ensure low-latency transmission.

[0008] Step S3: Wireless channel transmission and anti-interference, audio data is transmitted through wireless channels, orthogonal frequency division multiplexing and high-order modulation technology are used to improve the transmission spectrum efficiency, and spread spectrum technology and adaptive interference elimination algorithm are used to combat channel interference such as narrowband interference and multipath fading to ensure the stability of sound quality during transmission;

[0009] Step S4: Optimizing the congestion control algorithm, adjusting the transmission rate through the congestion control mechanism, and optimizing the processing process of network congestion;

[0010] Step S5: decoding and reconstruction at the receiving end, decoding and error correction of the received audio data packets at the receiving end, restoring the audio data by combining forward error correction (FEC) technology, and reconstructing the original audio signal by demodulation and dequantization to ensure audio quality;

[0011] Step S6: Dynamic buffer management and delay optimization. Through adaptive jitter buffer management, the buffer size is dynamically adjusted based on the real-time channel status and audio stream stability to avoid audio playback interruptions caused by network jitter and ensure low-latency transmission.

[0012] As a preferred technical solution of the present invention, compressing and encoding the audio signal includes:

[0013] Real-time monitoring of channel conditions, real-time monitoring of channel signal-to-noise ratio SNR, bit error rate BER and bandwidth B, and calculation of channel quality value CQ to evaluate the transmission quality of the wireless channel. The calculation formula of channel quality value is:

[0014] CQ = α1SNR nor +α2(1-BER nor )+α3B nor ;

[0015] Among them, α1, α2, and α3 are the weight coefficients of signal-to-noise ratio, bit error rate, and channel bandwidth, respectively, and their sum is 1. The subscript nor indicates that the parameters are normalized;

[0016] Channels are graded according to their quality values, and the channels are divided into different levels. The signal classification results are used as the basis for dynamic adjustment of coding parameters, and adaptive coding is adjusted according to the signal classification results;

[0017] Redundant information addition and forward error correction: adding an appropriate amount of redundant check information based on the bit error rate of the current channel, combined with forward error correction (FEC) technology to correct errors, and improve the ability to recover audio data under harsh channel conditions;

[0018] At the encoding end, by lowering the slow start threshold ssthresh, the slow start process time is reduced and the speed at which the algorithm enters the congestion avoidance phase is accelerated. After entering the congestion avoidance phase, the data volume changes from exponential growth to linear growth, thereby reducing data fluctuations in the network, increasing transmission stability, and reducing the probability of packet loss and retransmission, thereby reducing transmission delay.

[0019] As a preferred technical solution of the present invention, the adjustment of adaptive coding includes:

[0020] Adaptive adjustment of the coding bit rate and coding format. Dynamically adjust the audio coding bit rate R and coding format according to the channel classification results. If the channel classification result is good, a higher coding bit rate and a high-quality coding format are used to ensure high sound quality. If the channel classification result is poor, the coding bit rate is reduced, and a coding format with low latency and good anti-interference performance is used to improve transmission stability. The bit rate calculation formula is: R = R min +CQ(R max -R min );where R max and R min are the highest and lowest bitrates, respectively;

[0021] Dynamic adaptation of audio compression strategy. According to the channel conditions, an adaptive compression algorithm based on a psychoacoustic model is used. When the channel conditions are poor, a higher compression rate is achieved by discarding the audio part that is not sensitive to the human ear, so as to reduce the amount of transmitted data and ensure that the audio quality is not significantly affected. The length of the audio data frame is dynamically adjusted according to the channel conditions. When the channel conditions are good, a longer frame structure is used to reduce the transmission overhead. When the channel conditions are poor, a short frame structure is used to improve the transmission success rate.

[0022] Channel state adjustment based on channel feedback obtains real-time channel state information of the receiving end, including signal-to-noise ratio, bit error rate and packet loss rate, calculates the feedback channel quality value RCQ, divides the channel level again according to RCQ, adjusts the coding bit rate, coding format and compression strategy according to the corresponding results, realizes adaptive coding, and ensures the quality and stability of audio transmission.

[0023] As a preferred technical solution of the present invention, the adaptive compression algorithm includes:

[0024] Spectral analysis, through fast Fourier transform, extracts the frequency components of the audio signal and identifies the various frequency components and their amplitudes in the audio;

[0025] The construction of the psychoacoustic model divides the audio spectrum into 24 frequency bands based on the Bark scale to reflect the sensitivity of the human ear to different frequencies; calculates the masking threshold M, calculates the masking effect of each frequency component, and determines whether the sound of a certain frequency will be masked by a stronger adjacent frequency based on the existing psychoacoustic model formula;

[0026] The calculation formula of the masking threshold M is: M(f) = P(f) - Δ(f); where P(f) is the strong signal power at frequency f, and Δ(f) is the masking effect of the adjacent frequency;

[0027] Calculate the comparison between the signal and the masking threshold. For each frequency component, compare its actual amplitude with the corresponding masking threshold. If the amplitude of a frequency component is lower than the masking threshold, it means that the signal is imperceptible to the human ear and can be discarded. It is encoded using a lower-precision quantization method. If the signal amplitude is much higher than the masking threshold, it is retained as important information and encoded using a high-precision quantization method.

[0028] The encoded audio data is compressed using a dynamically adjusted compression ratio. The compression ratio is calculated as: C d =C(1-f(CQ)); where C is the basic compression ratio and f(CQ) represents the adjustment function based on the channel quality value.

[0029] As a preferred technical solution of the present invention, real-time adjustment of time slot resources includes:

[0030] Classify the service quality requirements of data packets, including delay-sensitive, bandwidth-sensitive, reliability-sensitive and low-priority data packets; dynamically manage the time slot resource pool, divide the time slot resources into multiple time slots, allocate data packets of different priorities on demand through the time slot resource pool, and adopt the shortest remaining time priority scheduling algorithm combined with the maximum bandwidth allocation to give priority to meeting the low-delay requirements of high-priority data packets; based on network feedback, dynamically adjust the time slot allocation strategy of various types of data packets to ensure transmission efficiency and stability; allow the interruption of low-priority data packets when necessary, and give priority to allocating time slots to high-priority data packets to ensure low-delay transmission; reschedule the preempted low-priority data packets to ensure the rational use and fair allocation of system resources.

[0031] As a preferred technical solution of the present invention, wireless channel transmission and anti-interference include:

[0032] Orthogonal frequency division multiplexing technology is used to divide the transmission bandwidth into multiple mutually orthogonal subcarriers to transmit audio data in parallel; high-order modulation technology is used to adaptively select the modulation order according to the channel status to increase the transmission rate when the channel conditions are good, and reduce the modulation order when the channel conditions deteriorate to ensure the stability of transmission and the reliability of data; audio signals are transmitted using an extended spectrum range,

[0033] Adopting adaptive interference elimination algorithm, real-time detection of interference sources in the channel, eliminating narrowband interference, random noise, multipath fading and other channel interference through adaptive filtering and interference suppression technology, and optimizing the signal quality of the transmission channel;

[0034] Combined with the frequency selective fading model, diversity technology is used to reduce the impact of multipath fading and improve the transmission reliability of audio data in complex wireless environments.

[0035] As a preferred technical solution of the present invention, the adaptive interference elimination algorithm includes:

[0036] The received signal strength (RSSI) of the receiving end is obtained, and combined with the channel quality value, the current interference level and channel condition are evaluated; based on the current channel status, a pattern recognition algorithm is used to classify and identify the interference type, which includes narrowband interference, broadband interference, multipath fading, and adjacent channel interference; according to the identified interference type, the interference suppression strategy is dynamically selected and adjusted.

[0037] As a preferred technical solution of the present invention, the congestion control algorithm optimization includes:

[0038] The data flow in wireless audio transmission is managed through a congestion control algorithm based on packet loss. In the initial stage, a slow start algorithm is used to quickly increase the sending window and monitor packet loss. If packet loss occurs, fast retransmission and fast recovery algorithms are executed to avoid long timeout retransmissions, thereby reducing delays. Slow start optimization: at the beginning of each transmission, the slow start threshold (ssthresh) is adjusted to reduce the transmission delay in the slow start phase and accelerate the entry into the congestion avoidance phase to reduce the impact of sudden network fluctuations on the stability of audio stream transmission. Congestion avoidance and recovery: when network congestion is detected, the congestion avoidance phase is entered to reduce the occurrence of congestion by linearly increasing the transmission window. At the same time, when network jitter occurs, timely recovery is performed to prevent excessive packet loss from causing audio stream interruption.

[0039] As a preferred technical solution of the present invention, the decoding and reconstruction at the receiving end includes:

[0040] At the transmitting end, an appropriate error correction coding scheme is selected according to the channel conditions. The error correction codes include convolutional codes, Reed-Solomon codes and low-density parity check codes. The original audio data is divided into blocks. According to the selected forward error correction coding algorithm, redundant check information is calculated for each data block and attached to the end of the audio data to form a coded data packet with redundant information. The forward error correction coded data packet is transmitted through the wireless channel.

[0041] After receiving a data packet with redundant information, the receiving end detects errors in the data packet through the embedded redundant information; according to the type of redundant check information attached to the received data packet, the corresponding error correction algorithm is used to correct the error; after successfully correcting the erroneous data, the receiving end performs a decoding operation, separates the corrected audio data from the redundant information, and restores the original unencoded audio data.

[0042] As a preferred technical solution of the present invention, dynamic buffer management and delay optimization include:

[0043] An adaptive jitter buffer is configured at the receiving end to alleviate the instability of the audio stream caused by jitter or instantaneous packet loss during network transmission. The buffer is dynamically adjusted according to the real-time monitored network delay, jitter and packet loss rate to ensure smooth audio playback.

[0044] Network status monitoring and analysis: real-time monitoring of network status parameters in audio data transmission, including delay, jitter, packet loss rate, and bandwidth fluctuations. By analyzing network status parameters, decisions on buffer size adjustment are obtained. When network delay or jitter exceeds a predetermined threshold, the buffer size automatically increases to cope with potential data arrival delays. When network conditions return to normal, the buffer size gradually decreases to reduce playback delays. Combining historical network status data and real-time monitoring data, a machine learning-based prediction model is used to predict future network jitter or delay fluctuations, and the buffer size is adjusted in advance to prevent audio playback interruptions.

[0045] Combined with the order in which packets arrive and are decoded, the received audio data is sorted and cached to ensure that the packets are played in the correct timestamp order for audio playback.

[0046] The present invention has the following advantages:

[0047] 1. The present invention ensures low-latency transmission of audio signals through multiple technologies such as priority scheduling, time slot allocation, dynamic buffer management and adaptive coding, and significantly improves the stability of audio transmission by combining congestion control, forward error correction (FEC) and adaptive interference cancellation technology. Even in complex wireless environments, the audio signal can maintain a high quality, and by quickly responding to channel changes, eliminating interference and correcting errors, it reduces the occurrence of packet loss and retransmission, thereby ensuring the stability of transmission.

[0048] 2. The present invention dynamically adjusts the encoding bit rate and compression strategy to ensure that a high-quality encoding format is used when channel conditions are good, thereby ensuring audio clarity and fidelity. By discarding unimportant audio information and using an adaptive compression algorithm, transmission efficiency is ensured while maintaining the highest possible sound quality. Through an adaptive compression algorithm based on a psychoacoustic model, when channel conditions are poor, audio information that is not sensitive to the human ear is automatically discarded, thereby reducing the amount of data and improving transmission efficiency. When channel conditions are good, an efficient encoding method is selected to further reduce unnecessary transmission overhead.

[0049] 3. The present invention utilizes orthogonal frequency division multiplexing (OFDM), high-order modulation technology and spread spectrum technology to improve transmission efficiency, and combines it with an adaptive interference elimination algorithm to effectively combat narrowband interference, broadband interference, multipath fading and other problems in the channel, thereby greatly improving the anti-interference capability of audio data transmission in complex wireless environments.

[0050] 4. The present invention uses adaptive coding, adaptive compression, adaptive interference elimination and adaptive buffer management technologies to enable the system to dynamically adjust according to the channel status and flexibly respond to sudden situations such as network fluctuations, interference, congestion, etc. The adaptive adjustment mechanism ensures that audio transmission can maintain good performance under different conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 The present invention is a flowchart of a low-delay adaptive wireless audio transmission method used in an embodiment of the present invention. DETAILED DESCRIPTION

[0052] In order to enable persons skilled in the art to better understand the technical solutions in the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0053] Embodiment 1, a low-delay adaptive wireless audio transmission method, see Figure 1 As shown, the following steps are included:

[0054] Step S1: audio signal acquisition and encoding, the audio signal is acquired, and an adaptive encoding strategy is used to compress and encode the audio signal based on the channel condition, wherein the encoding parameters are dynamically adjusted according to the channel condition to ensure the sound quality and transmission stability;

[0055] Compression encoding of audio signals includes:

[0056] Real-time monitoring of channel conditions, real-time monitoring of channel signal-to-noise ratio SNR, bit error rate BER and bandwidth B, and calculation of channel quality value CQ to evaluate the transmission quality of the wireless channel. The calculation formula of channel quality value is:

[0057] CQ = α1SNR nor +α2(1-BER nor )+α3B nor ;

[0058] Among them, α1, α2, and α3 are the weight coefficients of signal-to-noise ratio, bit error rate, and channel bandwidth, respectively, and their sum is 1. The subscript nor indicates that the parameters are normalized;

[0059] Channels are graded according to their quality values, and the channels are divided into different levels. The signal classification results are used as the basis for dynamic adjustment of coding parameters, and adaptive coding is adjusted according to the signal classification results;

[0060] Redundant information addition and forward error correction: adding an appropriate amount of redundant check information based on the bit error rate of the current channel, combined with forward error correction (FEC) technology to correct errors, and improve the ability to recover audio data under harsh channel conditions;

[0061] At the encoding end, by lowering the slow start threshold ssthresh, the slow start process time is reduced and the speed at which the algorithm enters the congestion avoidance phase is accelerated. After entering the congestion avoidance phase, the data volume changes from exponential growth to linear growth, thereby reducing data fluctuations in the network, increasing transmission stability, and reducing the probability of packet loss and retransmission, thereby reducing transmission delay.

[0062] The adjustments for adaptive encoding include:

[0063] Adaptive adjustment of the coding bit rate and coding format. Dynamically adjust the audio coding bit rate R and coding format according to the channel classification results. If the channel classification result is good, a higher coding bit rate and a high-quality coding format are used to ensure high sound quality. If the channel classification result is poor, the coding bit rate is reduced, and a coding format with low latency and good anti-interference performance is used to improve transmission stability. The bit rate calculation formula is: R = R min +CQ(R max -R min );where R max and R min are the highest and lowest bitrates, respectively;

[0064] Dynamic adaptation of audio compression strategy. According to the channel conditions, an adaptive compression algorithm based on a psychoacoustic model is used. When the channel conditions are poor, a higher compression rate is achieved by discarding the audio part that is not sensitive to the human ear, so as to reduce the amount of transmitted data and ensure that the audio quality is not significantly affected. The length of the audio data frame is dynamically adjusted according to the channel conditions. When the channel conditions are good, a longer frame structure is used to reduce the transmission overhead. When the channel conditions are poor, a short frame structure is used to improve the transmission success rate.

[0065] Channel state adjustment based on channel feedback obtains real-time channel state information of the receiving end, including signal-to-noise ratio, bit error rate and packet loss rate, calculates the feedback channel quality value RCQ, divides the channel level again according to RCQ, adjusts the coding bit rate, coding format and compression strategy according to the corresponding results, realizes adaptive coding, and ensures the quality and stability of audio transmission.

[0066] Adaptive compression algorithms include:

[0067] Spectral analysis, through fast Fourier transform, extracts the frequency components of the audio signal and identifies the various frequency components and their amplitudes in the audio;

[0068] The construction of the psychoacoustic model divides the audio spectrum into 24 frequency bands based on the Bark scale to reflect the sensitivity of the human ear to different frequencies; calculates the masking threshold M, calculates the masking effect of each frequency component, and determines whether the sound of a certain frequency will be masked by a stronger adjacent frequency based on the existing psychoacoustic model formula;

[0069] The calculation formula of the masking threshold M is: M(f) = P(f) - Δ(f); where P(f) is the strong signal power at frequency f, and Δ(f) is the masking effect of the adjacent frequency;

[0070] Calculate the comparison between the signal and the masking threshold. For each frequency component, compare its actual amplitude with the corresponding masking threshold. If the amplitude of a frequency component is lower than the masking threshold, it means that the signal is imperceptible to the human ear and can be discarded. It is encoded using a lower-precision quantization method. If the signal amplitude is much higher than the masking threshold, it is retained as important information and encoded using a high-precision quantization method.

[0071] The encoded audio data is compressed using a dynamically adjusted compression ratio. The compression ratio is calculated as: C d =C(1-f(CQ)); where C is the basic compression ratio and f(CQ) represents the adjustment function based on the channel quality value.

[0072] Step S2: Priority scheduling and time slot allocation: Priority scheduling is performed based on the media access control (MAC) layer. The time slot resources are adjusted in real time according to the service quality requirements of the data packet through a dynamic time slot allocation algorithm to ensure low-latency transmission.

[0073] Real-time adjustment of time slot resources includes:

[0074] Classify the service quality requirements of data packets, including delay-sensitive, bandwidth-sensitive, reliability-sensitive and low-priority data packets; dynamically manage the time slot resource pool, divide the time slot resources into multiple time slots, allocate data packets of different priorities on demand through the time slot resource pool, and adopt the shortest remaining time priority scheduling algorithm combined with the maximum bandwidth allocation to give priority to meeting the low-delay requirements of high-priority data packets; based on network feedback, dynamically adjust the time slot allocation strategy of various types of data packets to ensure transmission efficiency and stability; allow the interruption of low-priority data packets when necessary, and give priority to allocating time slots to high-priority data packets to ensure low-delay transmission; reschedule the preempted low-priority data packets to ensure the rational use and fair allocation of system resources.

[0075] Step S3: Wireless channel transmission and anti-interference, audio data is transmitted through wireless channels, orthogonal frequency division multiplexing and high-order modulation technology are used to improve the transmission spectrum efficiency, and spread spectrum technology and adaptive interference elimination algorithm are used to combat channel interference such as narrowband interference and multipath fading to ensure the stability of sound quality during transmission;

[0076] Wireless channel transmission and anti-interference include:

[0077] Orthogonal frequency division multiplexing technology is used to divide the transmission bandwidth into multiple mutually orthogonal subcarriers to transmit audio data in parallel; high-order modulation technology is used to adaptively select the modulation order according to the channel status to increase the transmission rate when the channel conditions are good, and reduce the modulation order when the channel conditions deteriorate to ensure the stability of transmission and the reliability of data; audio signals are transmitted using an extended spectrum range,

[0078] Adopting adaptive interference elimination algorithm, real-time detection of interference sources in the channel, eliminating narrowband interference, random noise, multipath fading and other channel interference through adaptive filtering and interference suppression technology, and optimizing the signal quality of the transmission channel;

[0079] Combined with the frequency selective fading model, diversity technology is used to reduce the impact of multipath fading and improve the transmission reliability of audio data in complex wireless environments.

[0080] The adaptive interference cancellation algorithm includes:

[0081] The received signal strength (RSSI) of the receiving end is obtained, and combined with the channel quality value, the current interference level and channel condition are evaluated; based on the current channel status, a pattern recognition algorithm is used to classify and identify the interference type, which includes narrowband interference, broadband interference, multipath fading, and adjacent channel interference; according to the identified interference type, the interference suppression strategy is dynamically selected and adjusted.

[0082] Step S4: Optimizing the congestion control algorithm, adjusting the transmission rate through the congestion control mechanism, and optimizing the processing process of network congestion;

[0083] Congestion control algorithm optimization includes:

[0084] The data flow in wireless audio transmission is managed through a congestion control algorithm based on packet loss. In the initial stage, a slow start algorithm is used to quickly increase the sending window and monitor packet loss. If packet loss occurs, fast retransmission and fast recovery algorithms are executed to avoid long timeout retransmissions, thereby reducing delays. Slow start optimization: at the beginning of each transmission, the slow start threshold (ssthresh) is adjusted to reduce the transmission delay in the slow start phase and accelerate the entry into the congestion avoidance phase to reduce the impact of sudden network fluctuations on the stability of audio stream transmission. Congestion avoidance and recovery: when network congestion is detected, the congestion avoidance phase is entered to reduce the occurrence of congestion by linearly increasing the transmission window. At the same time, when network jitter occurs, timely recovery is performed to prevent excessive packet loss from causing audio stream interruption.

[0085] Step S5: decoding and reconstruction at the receiving end, decoding and error correction of the received audio data packets at the receiving end, restoring the audio data by combining forward error correction (FEC) technology, and reconstructing the original audio signal by demodulation and dequantization to ensure audio quality;

[0086] The decoding and reconstruction at the receiving end include:

[0087] At the transmitting end, an appropriate error correction coding scheme is selected according to the channel conditions. The error correction codes include convolutional codes, Reed-Solomon codes and low-density parity check codes. The original audio data is divided into blocks. According to the selected forward error correction coding algorithm, redundant check information is calculated for each data block and attached to the end of the audio data to form a coded data packet with redundant information. The forward error correction coded data packet is transmitted through the wireless channel.

[0088] After receiving a data packet with redundant information, the receiving end detects errors in the data packet through the embedded redundant information; according to the type of redundant check information attached to the received data packet, the corresponding error correction algorithm is used to correct the error; after successfully correcting the erroneous data, the receiving end performs a decoding operation, separates the corrected audio data from the redundant information, and restores the original unencoded audio data.

[0089] Step S6: Dynamic buffer management and delay optimization. Through adaptive jitter buffer management, the buffer size is dynamically adjusted based on the real-time channel status and audio stream stability to avoid audio playback interruptions caused by network jitter and ensure low-latency transmission.

[0090] Dynamic buffer management and latency optimization include:

[0091] An adaptive jitter buffer is configured at the receiving end to alleviate the instability of the audio stream caused by jitter or instantaneous packet loss during network transmission. The buffer is dynamically adjusted according to the real-time monitored network delay, jitter and packet loss rate to ensure smooth audio playback.

[0092] Network status monitoring and analysis: real-time monitoring of network status parameters in audio data transmission, including delay, jitter, packet loss rate, and bandwidth fluctuations. By analyzing network status parameters, decisions on buffer size adjustment are obtained. When network delay or jitter exceeds a predetermined threshold, the buffer size automatically increases to cope with potential data arrival delays. When network conditions return to normal, the buffer size gradually decreases to reduce playback delays. Combining historical network status data and real-time monitoring data, a machine learning-based prediction model is used to predict future network jitter or delay fluctuations, and the buffer size is adjusted in advance to prevent audio playback interruptions.

[0093] Combined with the order in which packets arrive and are decoded, the received audio data is sorted and cached to ensure that the packets are played in the correct timestamp order for audio playback.

[0094] Embodiment 2, a low-delay adaptive wireless audio transmission method, the specific implementation steps are as follows:

[0095] Step S1: Audio signal acquisition and encoding

[0096] In this embodiment, the audio signal is collected by a high-fidelity microphone, and the sampling rate of the microphone is set to 44.1kHz to ensure the high fidelity of the sound quality. After the audio is collected, the audio signal is compressed and encoded using an adaptive coding strategy. The adaptive coding strategy monitors the signal-to-noise ratio (SNR), bit error rate (BER) and bandwidth (B) of the channel in real time based on the current wireless channel conditions. The channel monitoring frequency is once every 100ms to ensure the real-time nature of audio transmission.

[0097] When the channel signal is good, the system will automatically select a higher encoding bit rate, such as 320kbps, and use a high-quality encoding format (such as AAC or Opus); when the channel condition is bad, the encoding bit rate will be reduced to 96kbps, and switch to a low-latency and anti-interference encoding format (such as SBC). When encoding, the forward error correction (FEC) technology will be combined to perform redundancy check on the audio data, adding additional redundant information to ensure effective audio data recovery even in the case of a high bit error rate.

[0098] Through this adaptive coding strategy, the coding parameters can be automatically adjusted under different channel conditions, thereby ensuring the stability of transmission while maintaining high sound quality.

[0099] Step S2: Priority scheduling and time slot allocation

[0100] In this embodiment, audio data packets are classified according to their quality of service requirements, specifically into delay-sensitive data packets (such as real-time voice), bandwidth-sensitive data packets (such as high-fidelity audio) and low-priority data packets (such as non-critical audio clips). The system uses the shortest remaining time first (SRTF) scheduling algorithm to allocate time slots, giving priority to meeting the low-delay transmission requirements of real-time voice and high-fidelity audio.

[0101] For example, in a congested network environment, time slot resources are allocated to real-time voice packets first, while the transmission rate of low-priority data packets is reduced. Through this scheduling mechanism, the system can ensure low-latency transmission of critical audio data while ensuring the rational use of system resources.

[0102] Step S3: Wireless channel transmission and anti-interference

[0103] Audio data is transmitted over a wireless channel using orthogonal frequency division multiplexing (OFDM). In this embodiment, OFDM divides the transmission bandwidth into multiple mutually orthogonal subcarriers, each of which carries part of the audio data. Based on channel conditions, the system uses adaptive modulation technology, such as using 64-QAM for modulation when the signal strength is good; when the signal is poor, it automatically switches to QPSK modulation to ensure signal reliability.

[0104] In addition, the system also incorporates an adaptive interference cancellation algorithm to identify interference sources such as narrowband interference and multipath fading by monitoring signal strength and channel status in real time. Interference cancellation uses an adaptive filter to suppress interference signals, thereby greatly improving the transmission quality of audio signals. In actual use, even in a multi-user or multi-interference source environment, this technology can still effectively combat channel interference and maintain a high level of audio transmission stability.

[0105] Step S4: Congestion control algorithm optimization

[0106] This embodiment uses a congestion control algorithm based on packet loss. In the initial stage of audio transmission, a slow start algorithm is used to quickly increase the sending window and monitor the packet loss rate in real time. Once packet loss is detected, the system immediately executes a fast retransmission and recovery strategy to avoid audio transmission delays caused by long waits for retransmission.

[0107] In actual network tests, when network jitter or congestion occurs, the congestion control algorithm can adjust the transmission window within 100ms, quickly enter the congestion avoidance phase, and reduce the packet loss rate to below 3%, thereby ensuring the continuity and stability of the audio stream.

[0108] Step S5: Decoding and reconstruction at the receiving end

[0109] At the receiving end, the system first uses forward error correction (FEC) technology to correct the received audio data. In this embodiment, Reed-Solomon code (RS code) is used for error correction. Through error correction technology, the receiving end can effectively recover damaged data caused by bit errors or packet loss during transmission.

[0110] The decoded data is dequantized and demodulated to reconstruct the original audio signal. To ensure high audio fidelity, the system uses a layered decoding structure after error correction, first decoding high-priority audio data packets to ensure the recovery of critical audio data.

[0111] Step S6: Dynamic buffer management and latency optimization

[0112] In order to adapt to changes in network status, the receiving end is configured with an adaptive jitter buffer. In this embodiment, the buffer is dynamically adjusted according to the real-time monitoring of network delay and jitter conditions. For example, when the system detects that the network delay is greater than 150ms, the buffer will automatically increase to prevent audio playback interruption; when the network condition returns to normal and the delay returns to within 50ms, the buffer will automatically decrease to reduce playback delay.

[0113] In addition, combined with machine learning models, historical network status data is used to predict future network jitter or delay fluctuations in advance, and the buffer size is adjusted dynamically. In experiments, the prediction model can accurately predict more than 90% of jitter changes, effectively avoiding interruptions in audio playback.

[0114] It should be understood that those skilled in the art can make improvements or changes based on the above description, and all such improvements and changes should fall within the scope of protection of the appended claims of the present invention. Parts not described in detail in this specification belong to the prior art known to those skilled in the art.

Claims

1. A low-delay adaptive wireless audio transmission method, characterized in that: The following steps are involved: Step S1: audio signal acquisition and encoding, the audio signal is acquired, and an adaptive encoding strategy is used to compress and encode the audio signal based on the channel condition, wherein the encoding parameters are dynamically adjusted according to the channel condition to ensure the sound quality and transmission stability; Step S2: Priority scheduling and time slot allocation: Priority scheduling is performed based on the media access control (MAC) layer. The time slot resources are adjusted in real time according to the service quality requirements of the data packet through a dynamic time slot allocation algorithm to ensure low-latency transmission. Step S3: Wireless channel transmission and anti-interference, audio data is transmitted through wireless channels, orthogonal frequency division multiplexing and high-order modulation technology are used to improve the transmission spectrum efficiency, and spread spectrum technology and adaptive interference elimination algorithm are used to combat channel interference such as narrowband interference and multipath fading to ensure the stability of sound quality during transmission; Step S4: Optimizing the congestion control algorithm, adjusting the transmission rate through the congestion control mechanism, and optimizing the processing process of network congestion; Step S5: decoding and reconstruction at the receiving end, decoding and error correction of the received audio data packets at the receiving end, restoring the audio data by combining forward error correction (FEC) technology, and reconstructing the original audio signal by demodulation and dequantization to ensure audio quality; Step S6: Dynamic buffer management and delay optimization. Through adaptive jitter buffer management, the buffer size is dynamically adjusted based on the real-time channel status and audio stream stability to avoid audio playback interruptions caused by network jitter and ensure low-latency transmission.

2. A low-delay adaptive wireless audio transmission method according to claim 1, characterized in that: Compression encoding of audio signals includes: Real-time monitoring of channel conditions, real-time monitoring of channel signal-to-noise ratio SNR, bit error rate BER and bandwidth B, and calculation of channel quality value CQ to evaluate the transmission quality of the wireless channel. The calculation formula of channel quality value is: CQ=α1SNR nor +α2(1-BER nor )+α3B nor ; Among them, α1, α2, and α3 are the weight coefficients of signal-to-noise ratio, bit error rate, and channel bandwidth, respectively, and their sum is 1. The subscript nor indicates that the parameters are normalized; Channels are graded according to their quality values, and the channels are divided into different levels. The signal classification results are used as the basis for dynamic adjustment of coding parameters, and adaptive coding is adjusted according to the signal classification results; Redundant information addition and forward error correction: adding an appropriate amount of redundant check information based on the bit error rate of the current channel, combined with forward error correction (FEC) technology to correct errors, and improve the ability to recover audio data under harsh channel conditions; At the encoding end, by lowering the slow start threshold ssthresh, the slow start process time is reduced and the speed at which the algorithm enters the congestion avoidance phase is accelerated. After entering the congestion avoidance phase, the data volume changes from exponential growth to linear growth, thereby reducing data fluctuations in the network, increasing transmission stability, and reducing the probability of packet loss and retransmission, thereby reducing transmission delay.

3. A low-delay adaptive wireless audio transmission method according to claim 2, characterized in that: The adjustments for adaptive encoding include: Adaptive adjustment of the coding bit rate and coding format. Dynamically adjust the audio coding bit rate R and coding format according to the channel classification results. If the channel classification result is good, a higher coding bit rate and a high-quality coding format are used to ensure high sound quality. If the channel classification result is poor, the coding bit rate is reduced, and a coding format with low latency and good anti-interference performance is used to improve transmission stability. The bit rate calculation formula is: R = R min +CQ(R max -R min );where R max and R min are the highest and lowest bitrates, respectively; Dynamic adaptation of audio compression strategy. According to the channel conditions, an adaptive compression algorithm based on a psychoacoustic model is used. When the channel conditions are poor, a higher compression rate is achieved by discarding the audio part that is not sensitive to the human ear, so as to reduce the amount of transmitted data and ensure that the audio quality is not significantly affected. The length of the audio data frame is dynamically adjusted according to the channel conditions. When the channel conditions are good, a longer frame structure is used to reduce the transmission overhead. When the channel conditions are poor, a short frame structure is used to improve the transmission success rate. Channel state adjustment based on channel feedback obtains real-time channel state information of the receiving end, including signal-to-noise ratio, bit error rate and packet loss rate, calculates the feedback channel quality value RCQ, divides the channel level again according to RCQ, adjusts the coding bit rate, coding format and compression strategy according to the corresponding results, realizes adaptive coding, and ensures the quality and stability of audio transmission.

4. A low-delay adaptive wireless audio transmission method according to claim 3, characterized in that: Adaptive compression algorithms include: Spectral analysis, through fast Fourier transform, extracts the frequency components of the audio signal and identifies the various frequency components and their amplitudes in the audio; The construction of the psychoacoustic model divides the audio spectrum into 24 frequency bands based on the Bark scale to reflect the sensitivity of the human ear to different frequencies; calculates the masking threshold M, calculates the masking effect of each frequency component, and determines whether the sound of a certain frequency will be masked by a stronger adjacent frequency based on the existing psychoacoustic model formula; The calculation formula of masking threshold M is: ( f ) =P ( f ) -Δ(f); where P(f) is the strong signal power at frequency f, and Δ(f) is the masking effect of adjacent frequencies; Calculate the comparison between the signal and the masking threshold. For each frequency component, compare its actual amplitude with the corresponding masking threshold. If the amplitude of a frequency component is lower than the masking threshold, it means that the signal is imperceptible to the human ear and can be discarded. It is encoded using a lower-precision quantization method. If the signal amplitude is much higher than the masking threshold, it is retained as important information and encoded using a high-precision quantization method. The encoded audio data is compressed using a dynamically adjusted compression ratio. The compression ratio is calculated as: C d =C(1-f(CQ)); where C is the basic compression ratio and f(CQ) represents the adjustment function based on the channel quality value.

5. A low-delay adaptive wireless audio transmission method according to claim 1, characterized in that: Real-time adjustment of time slot resources includes: Classify the service quality requirements of data packets, including delay-sensitive, bandwidth-sensitive, reliability-sensitive and low-priority data packets; dynamically manage the time slot resource pool, divide the time slot resources into multiple time slots, allocate data packets of different priorities on demand through the time slot resource pool, and adopt the shortest remaining time priority scheduling algorithm combined with the maximum bandwidth allocation to give priority to meeting the low-delay requirements of high-priority data packets; based on network feedback, dynamically adjust the time slot allocation strategy of various types of data packets to ensure transmission efficiency and stability; allow the interruption of low-priority data packets when necessary, and give priority to allocating time slots to high-priority data packets to ensure low-delay transmission; reschedule the preempted low-priority data packets to ensure the rational use and fair allocation of system resources.

6. A low-delay adaptive wireless audio transmission method according to claim 1, characterized in that: Wireless channel transmission and anti-interference include: Orthogonal frequency division multiplexing technology is used to divide the transmission bandwidth into multiple mutually orthogonal subcarriers to transmit audio data in parallel; high-order modulation technology is used to adaptively select the modulation order according to the channel status to increase the transmission rate when the channel conditions are good, and reduce the modulation order when the channel conditions deteriorate to ensure the stability of transmission and the reliability of data; audio signals are transmitted using an extended spectrum range, Adopting adaptive interference elimination algorithm, real-time detection of interference sources in the channel, eliminating narrowband interference, random noise, multipath fading and other channel interference through adaptive filtering and interference suppression technology, and optimizing the signal quality of the transmission channel; Combined with the frequency selective fading model, diversity technology is used to reduce the impact of multipath fading and improve the transmission reliability of audio data in complex wireless environments.

7. A low-delay adaptive wireless audio transmission method according to claim 6, characterized in that: The adaptive interference cancellation algorithm includes: The received signal strength (RSSI) of the receiving end is obtained, and combined with the channel quality value, the current interference level and channel condition are evaluated; based on the current channel status, a pattern recognition algorithm is used to classify and identify the interference type, which includes narrowband interference, broadband interference, multipath fading, and adjacent channel interference; according to the identified interference type, the interference suppression strategy is dynamically selected and adjusted.

8. A low-delay adaptive wireless audio transmission method according to claim 1, characterized in that: Congestion control algorithm optimization includes: The data flow in wireless audio transmission is managed through a congestion control algorithm based on packet loss. In the initial stage, a slow start algorithm is used to quickly increase the sending window and monitor packet loss. If packet loss occurs, fast retransmission and fast recovery algorithms are executed to avoid long timeout retransmissions, thereby reducing delays. Slow start optimization: at the beginning of each transmission, the slow start threshold (ssthresh) is adjusted to reduce the transmission delay in the slow start phase and accelerate the entry into the congestion avoidance phase to reduce the impact of sudden network fluctuations on the stability of audio stream transmission. Congestion avoidance and recovery: when network congestion is detected, the congestion avoidance phase is entered to reduce the occurrence of congestion by linearly increasing the transmission window. At the same time, when network jitter occurs, timely recovery is performed to prevent excessive packet loss from causing audio stream interruption.

9. A low-delay adaptive wireless audio transmission method according to claim 1, characterized in that: The decoding and reconstruction at the receiving end include: At the transmitting end, an appropriate error correction coding scheme is selected according to the channel conditions. The error correction codes include convolutional codes, Reed-Solomon codes and low-density parity check codes. The original audio data is divided into blocks. According to the selected forward error correction coding algorithm, redundant check information is calculated for each data block and attached to the end of the audio data to form a coded data packet with redundant information. The forward error correction coded data packet is transmitted through the wireless channel. After receiving a data packet with redundant information, the receiving end detects errors in the data packet through the embedded redundant information; according to the type of redundant check information attached to the received data packet, the corresponding error correction algorithm is used to correct the error; after successfully correcting the erroneous data, the receiving end performs a decoding operation, separates the corrected audio data from the redundant information, and restores the original unencoded audio data.

10. A low-delay adaptive wireless audio transmission method according to claim 1, characterized in that: Dynamic buffer management and latency optimization include: An adaptive jitter buffer is configured at the receiving end to alleviate the instability of the audio stream caused by jitter or instantaneous packet loss during network transmission. The buffer is dynamically adjusted according to the real-time monitored network delay, jitter and packet loss rate to ensure smooth audio playback. Network status monitoring and analysis: real-time monitoring of network status parameters in audio data transmission, including delay, jitter, packet loss rate, and bandwidth fluctuations. By analyzing network status parameters, decisions on buffer size adjustment are obtained. When network delay or jitter exceeds a predetermined threshold, the buffer size automatically increases to cope with potential data arrival delays. When network conditions return to normal, the buffer size gradually decreases to reduce playback delays. Combining historical network status data and real-time monitoring data, a machine learning-based prediction model is used to predict future network jitter or delay fluctuations, and the buffer size is adjusted in advance to prevent audio playback interruptions. Combined with the order in which packets arrive and are decoded, the received audio data is sorted and cached to ensure that the packets are played in the correct timestamp order for audio playback.

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