Wireless audio and sound effect processing method and system

Abstract

The invention provides a wireless audio sound effect processing method and system, and relates to the technical field of audio processing, and the method comprises the steps: collecting wireless audio data; whether noise exists in the wireless audio data or not is judged through an autoregression prediction algorithm, and if yes, noise signals in the wireless audio data are extracted; in combination with a sparse dictionary learning algorithm, generating a reverse noise signal for counteracting the noise signal through a filter with an adaptive filtering step length; aligning the noise signal and the reverse noise signal based on the dynamic phase compensation order; synthesizing the wireless audio data and the aligned reverse noise signal to obtain optimized wireless audio data; and otherwise, marking the wireless audio data as the optimized wireless audio data, and outputting the optimized wireless audio data. While the noise reduction efficiency is improved, complex and changeable noise information can be effectively handled, and the wireless audio quality is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of audio processing, and in particular to a wireless audio sound effect processing method and system. Background Art

[0002] With the rapid development of human-computer interaction and wireless devices, noise reduction technology is now used in microphones and Bluetooth headsets to improve audio collection or audio playback. Noise reduction technology can effectively remove background noise, improve voice clarity, optimize sound quality experience, and ensure high-quality sound transmission even in noisy environments.

[0003] Audio noise reduction is crucial to improving audio quality, especially in modern communications, speech recognition, broadcasting and entertainment applications, where noise can greatly interfere with information transmission. Noise not only affects the user's auditory experience, but can also cause signal distortion and reduce the accuracy and efficiency of the system. Through noise reduction technology, effective information in audio signals can be clearly extracted, the reliability and accuracy of the speech recognition system can be enhanced, and the quality of hearing can be improved, while avoiding background noise interference with communication.

[0004] However, existing noise reduction methods often use fixed noise reduction parameters to perform audio noise reduction in different complex noise environments, which may lead to loss of sound quality, especially in the case of transient noise or high-frequency noise. Summary of the invention

[0005] In order to solve the technical problem that the existing noise reduction methods in the prior art often use fixed noise reduction parameters to perform audio noise reduction in different complex noise environments, which may cause sound quality loss, especially poor effect in transient noise or high-frequency noise conditions, the present invention provides a wireless audio sound effect processing method and system.

[0006] The technical solution provided by the embodiment of the present invention is as follows: First aspect An embodiment of the present invention provides a wireless audio effect processing method, comprising: S1: Collect wireless audio data; S2: Determine whether the wireless audio data has noise by using an autoregressive prediction algorithm. If yes, proceed to step S3; otherwise, mark the wireless audio data as optimized wireless audio data and proceed to step S7; S3: extracting noise signals from wireless audio data; S4: Combined with the sparse dictionary learning algorithm, a reverse noise signal is generated through a filter with an adaptive filtering step size to cancel the noise signal; S5: Align the noise signal and the reverse noise signal based on the dynamic phase compensation order; S6: synthesizing the wireless audio data and the aligned reverse noise signal to obtain optimized wireless audio data; S7: Output optimized wireless audio data.

[0007] Second aspect An embodiment of the present invention provides a wireless audio and sound effect processing system, comprising: processor; A memory, wherein computer-readable instructions are stored in the memory, and when the computer-readable instructions are executed by the processor, the wireless audio sound effect processing method as described in the first aspect is implemented.

[0008] The third aspect An embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, the wireless audio sound effect processing method as described in the first aspect is implemented.

[0009] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least: In the embodiment of the present invention, the autoregressive prediction algorithm is first used to perform a pre-noise judgment on the collected wireless audio data. Noise reduction can only be performed when it is determined that there is noise. By analyzing the autocorrelation characteristics of the audio data, the presence of noise is accurately judged to avoid noise reduction in the absence of noise, thereby improving the noise reduction efficiency and audio quality, effectively avoiding unnecessary noise reduction processing, improving calculation efficiency, reducing audio loss caused by global noise reduction, and improving the accuracy of noise judgment. In the process of noise reduction, a sparse dictionary learning algorithm is combined, and a filter with an adaptive filtering step size is used to generate a reverse noise signal for offsetting the noise signal. The sparse dictionary learning algorithm can effectively extract noise components by learning noise features and performing sparse representation, and the adaptive filtering step size dynamically adjusts the filter according to noise changes, thereby achieving more accurate noise elimination and better audio quality. The noise signal and the reverse noise signal are aligned based on the dynamic phase compensation order, and then the wireless audio data and the aligned reverse noise signal are synthesized to obtain optimized wireless audio data and output. It can accurately judge noise, dynamically adjust filters and extract noise features, thereby effectively improving the accuracy of noise elimination, avoiding unnecessary noise reduction processing, and improving audio quality and processing efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0011] Figure 1 A flowchart of a wireless audio effect processing method provided by an embodiment of the present invention;

[0012] Figure 2 A schematic diagram of the structure of a wireless audio and sound effect processing system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0013] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0014] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "example" in the present invention should not be interpreted as being better or more advantageous than other embodiments or designs. Specifically, the use of the word "example" is intended to present the concept in a specific way. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or it can be either of the two.

[0015] In order to make the technical problems, technical solutions and advantages to be solved by the present invention more clear, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0016] Reference Manual Attached Figure 1 , shows a flow chart of a wireless audio sound effect processing method provided by an embodiment of the present invention.

[0017] The embodiment of the present invention provides a wireless audio sound effect processing method, which can be implemented by a wireless audio sound effect processing system device, and the wireless audio sound effect processing system device can be a terminal or a server. The processing flow of the wireless audio sound effect processing method can include the following steps:

[0018] S1: Collect wireless audio data.

[0019] Among them, wireless audio data refers to audio signals transmitted through wireless signals, which do not rely on wired connections and are usually transmitted from audio sources (such as microphones, headphones) to receiving devices through wireless communication technologies such as Bluetooth, Wi-Fi or other wireless protocols. Wireless audio data may include audio acquisition data from a microphone, or it may be audio data acquired by headphones through a built-in microphone, including the output audio signal of the headphones and the reverse audio signal for noise reduction. Wireless audio data is collected through wireless devices (such as microphones or Bluetooth headphones). These data include audio signals received from the environment and sounds collected by the built-in microphone of the headphones, which are used to achieve more efficient noise reduction. The collected audio data will be used as input for subsequent noise reduction processing to ensure that noise signals can be effectively identified and removed.

[0020] S2: Determine whether there is noise in the wireless audio data through an autoregressive prediction algorithm. If yes, proceed to step S3; otherwise, mark the wireless audio data as optimized wireless audio data and proceed to step S7.

[0021] Among them, the autoregressive prediction algorithm is a statistical model that predicts future values ​​based on current and past signal values. The autoregressive model assumes that the value of the current signal is a linear combination of the signals in the previous period. By estimating the autoregressive coefficient, the current signal can be predicted based on historical data, thereby identifying the noise component in the signal. The wireless audio data is analyzed by the autoregressive prediction algorithm to determine whether there is noise in the signal. The algorithm predicts future signal values ​​by analyzing the autocorrelation characteristics of the signal and compares them with the actual values ​​to identify noise. If noise is detected, the system enters the next step for noise reduction. If there is no noise, the wireless audio data is directly marked as optimized data and jumps to the subsequent steps to ensure efficiency and audio quality.

[0022] In a possible implementation, S201: framing the wireless audio data in combination with an autoregressive order related to the maximum noise length:

[0023] Among them, N w represents the frame length, p represents the autoregressive order of the autoregressive prediction algorithm, N max Indicates the maximum noise length, N h Indicates the degree of overlap between frames.

[0024] The maximum noise length refers to the longest time or the maximum number of samples that a noise signal may last in a wireless audio signal. It helps determine the order of the autoregressive prediction model to better capture the characteristics of noise. The order of the autoregressive prediction algorithm is determined by combining the maximum noise length, and then the frame length and inter-frame overlap are determined. In this way, the audio signal can be accurately divided and the noise-related features can be extracted, ensuring the accuracy of noise judgment and the effectiveness of noise reduction processing.

[0025] S202: Calculate the autocorrelation coefficient of each frame of wireless audio data obtained by framing at different lag orders and the noise variance related to the prediction error of the autoregressive prediction algorithm:

[0026] in, represents the estimated value of the autocorrelation function of the wireless audio data under the lag coefficient τ for each frame of wireless audio data, N represents the total number of samples in the wireless audio data of the current frame, x k and x k-τ Respectively represent the kth wireless audio data value and the k-τth wireless audio data value in the wireless audio data of the current frame, ai Represents the autocorrelation coefficient of the wireless audio data of the current frame at the i-th order, i=1,2,p, represents the noise variance, e k Represents the prediction error of the kth wireless audio data value in the wireless audio data of the current frame.

[0027] It should be noted that by calculating the autoregressive coefficient and the noise variance, the characteristics of the audio data, especially the behavior of the noise, can be accurately evaluated and modeled. By estimating the noise variance of the autocorrelation function and predicting the error, the model parameters can be dynamically adjusted to optimize the noise identification and removal effect, ensuring that the noise reduction process is efficient and has low error, while avoiding unnecessary signal loss, and improving the accuracy of noise reduction and audio quality.

[0028] S203: Calculate the detection signal difference between the actual value of the wireless audio data and the predicted value of the wireless audio data in combination with the calculated autocorrelation coefficient:

[0029] in, Indicates the actual value of the wireless audio data of the current frame at time t, represents the wireless audio data value at lag i, Represents the detection signal difference at time t.

[0030] S204: judging whether there is noise in the wireless audio data by using the noise standard deviation according to the detection signal difference:

[0031] Where I represents an exponential variable. When I=1, it means that the wireless audio data has noise. When I=0, it means that the wireless audio data has no noise. represents the noise standard deviation, Represents the noise filtering threshold.

[0032] Specifically, the process uses an autoregressive (AR) prediction algorithm to judge and analyze the noise of wireless audio data. First, the autoregressive order and the frame length are calculated according to the maximum noise length, and the audio data is divided into multiple frames. Then, the autocorrelation coefficient of each frame of audio data at different lag orders is calculated, and the noise variance is estimated. The prediction error of the signal is obtained by analyzing the autoregressive coefficient and the noise variance. The noise characteristics are further extracted by calculating the difference between the actual value and the predicted value of each frame. Finally, based on the difference between the detection signal and the noise standard deviation, the noise screening threshold is set to determine whether the signal has noise. If the difference exceeds the threshold, the system will consider that the frame contains noise and enter the subsequent noise reduction processing, otherwise it is marked as optimized audio data. The noise judgment and extraction by the autoregressive (AR) prediction algorithm can efficiently identify the noise components in wireless audio. By calculating the autoregressive coefficient and the noise variance, the noise and effective information in the signal can be accurately distinguished, thereby avoiding unnecessary noise reduction processing and improving processing efficiency. In addition, using noise standard deviation and threshold to determine the presence of noise helps improve the accuracy of noise identification, reduce audio signal distortion, and ensure optimized audio output without affecting sound quality.

[0033] Optionally, the noise screening threshold can be set to .

[0034] S3: Extract the noise signal from the wireless audio data.

[0035] It should be noted that the noise signal in the wireless audio data is extracted by analyzing the difference of the detection signal calculated by the autoregressive prediction algorithm. The noise signal refers to the part that is inconsistent with the original audio signal, which is regarded as background noise or interference. In this way, the noise can be effectively separated and prepared for the subsequent noise elimination processing, thereby ensuring the optimized audio quality.

[0036] In a possible implementation, S3 is specifically: The detection signal difference is extracted as a noise signal.

[0037] It should be noted that extracting the difference of the detection signal as the noise signal can accurately distinguish noise from valid signals. By directly extracting the noise signal, the misprocessing of the original audio is avoided, and the efficiency of noise removal and audio quality are improved.

[0038] S4: Combined with the sparse dictionary learning algorithm, a reverse noise signal for canceling the noise signal is generated through a filter with an adaptive filtering step size.

[0039] Among them, the sparse dictionary learning algorithm is a signal processing technology that aims to represent the characteristics of the signal with fewer non-zero coefficients by extracting a dictionary (i.e., basis vector) from the noise data and learning a sparse representation. This method can effectively separate noise from complex signals. Adaptive filtering step size refers to the filter dynamically adjusting the step size value according to the change of the noise signal. By selecting a suitable step size, the quality of the original audio signal can be retained to the greatest extent while removing noise. A filter is a tool for processing signals, which is used to filter out noise within a specific frequency range. An adaptive filter can dynamically adjust its coefficients according to the characteristics of the input signal to achieve the best noise removal effect. The reverse noise signal is a signal generated by the filter that is opposite to the noise signal, and the purpose is to cancel the noise by adding it. Through phase alignment and amplitude control, the reverse noise signal can effectively cancel the noise component in the original audio.

[0040] It should be noted that, by combining the sparse dictionary learning algorithm and the adaptive filter step size, the system generates an inverse noise signal by learning the sparse features of the noise signal and using the filter. Sparse dictionary learning enables the noise component to be accurately extracted and represented, while the adaptive filter step size adjusts the filter according to the dynamic changes of the noise signal, thereby generating an inverse signal that can effectively offset the noise. This process ensures more efficient noise elimination while minimizing sound quality loss.

[0041] In a possible implementation, S4 specifically includes: S401: Decompose the noise signal into multiple sub-frequency bands.

[0042] Alternatively, the decomposition may be performed using a wavelet transform.

[0043] S402: Perform sparse dictionary learning on each sub-band to obtain sparse coefficients corresponding to noise features:

[0044] in, The noise signal is decomposed into sub-bands, express The corresponding dictionary matrix, express The corresponding sparse coefficient vector, represents the regularization parameter representing the sparsity constraint weight, represents the square of the L2 norm, represents the L1 norm, It means to take the value that minimizes the function and .

[0045] It should be noted that by decomposing the noise signal into multiple sub-bands, the noise characteristics of each band can be processed independently, thereby improving the accuracy of noise suppression. The sparse dictionary learning algorithm can extract the sparse features of noise from each sub-band and optimize the sparse coefficients through the L1 norm and L2 norm to ensure more accurate noise labeling.

[0046] Optionally, Specifically, the sparse coefficient vector can be learned by the K-SVD algorithm Specifically, the steps of learning the sparse coefficient vector by the K-SVD algorithm include: first, initializing the dictionary matrix, and calculating the coefficient vector by sparse representation according to the current dictionary matrix and the noise signal in each iteration. Then, the coefficient vector is fixed, and the dictionary matrix is ​​updated using singular value decomposition (SVD) to make the dictionary more adaptable to the sparse representation of the signal. This process gradually improves the accuracy and effectiveness of noise identification by alternately optimizing the dictionary and sparse coefficients until the preset optimization goal is reached.

[0047] S403: Determine the adaptive filtering step size of the filter according to the sparse coefficient vector:

[0048] in, represents the number of non-zero elements in the sparse coefficient vector, Z represents the number of dictionary atoms, and Respectively represent the maximum filter step size and the minimum filter step size, Represents a constant that prevents the denominator from being zero.

[0049] The number of dictionary atoms is usually set to 256 by default. =10 -6 The maximum filter step size can be set to 0.1, and the minimum filter step size can be set to 0.001. Transient noise and steady-state noise can be distinguished by the noise change rate. Whether it exceeds the preset noise change rate is judged by the condition. Exceeds a preset noise change rate, where and Represents the sub-bands at time t and time t-1 respectively , it is considered as transient noise, otherwise, it is considered as steady-state noise, and then the corresponding adaptive filtering step size is selected. It can be understood that those skilled in the art can set the size of the preset noise change rate according to actual needs, and the present invention is not limited here.

[0050] It should be noted that by dynamically adjusting the step size of the filter according to the number of non-zero elements of the sparse coefficient vector and the energy of the noise signal, it is possible to flexibly deal with transient noise and steady-state noise. By setting the maximum and minimum step sizes, as well as the judgment of the noise change rate, the filter can optimize the filtering effect under different noise conditions, effectively eliminating noise while preventing over-filtering, thereby ensuring a balance between audio quality and processing efficiency.

[0051] In a possible implementation, when the noise change rate is greater than a preset noise change rate, the noise signal is determined to be transient noise; otherwise, the noise signal is determined to be steady-state noise.

[0052] The calculation formula of noise change rate is as follows:

[0053] in, Indicates the sub-band at time t The noise change rate, and Represents the sub-bands at time t and time t-1 respectively , it is considered as transient noise.

[0054] Specifically, the noise type is determined by calculating the noise change rate, effectively distinguishing transient noise from steady-state noise. If the noise change rate is greater than the preset value, it is considered transient noise, and the filtering strategy can be flexibly adjusted to enhance the response to sudden noise, while a more stable processing method is adopted for steady-state noise. In this way, the noise reduction effect can be optimized in different noise environments, and the clarity and processing efficiency of the audio signal can be improved.

[0055] S404: Determine the filter weight of each sub-band according to the error value between the sub-band correction value output by the filter and the actual value of the sub-band:

[0056] in, Indicates sub-band The error value at time t is Indicates sub-band The actual value of the sub-band at time t, and Represents sub-bands The filter weights at time t and time t+1.

[0057] It should be noted that dynamically adjusting the filter weights based on the error between the sub-band correction value and the actual value helps achieve more accurate noise cancellation. By calculating the error of each sub-band and combining it with its own adaptive filter step size, the filter response can be optimized to ensure that the filter can be effectively adjusted for different noise conditions, thereby improving the noise cancellation effect while minimizing signal distortion and improving audio quality.

[0058] S405: synthesizing each sub-frequency band obtained after filtering by a filter with an adaptive filtering step size and a filter weight to obtain a reverse noise signal.

[0059] It should be noted that by decomposing the noise signal into multiple sub-bands, the noise characteristics of each sub-band can be processed separately, thereby achieving more refined noise suppression. Using a sparse dictionary learning algorithm in a sub-band can effectively extract a sparse representation of the noise, and by learning the characteristics of the noise, accurately distinguish between noise and valid signals. In addition, combined with the dynamic adjustment of the adaptive filter step size and filter weights, the filter parameters can be flexibly adjusted according to different noise types (transient noise or steady-state noise), thereby ensuring more efficient noise elimination, reducing audio signal distortion and computational overhead, and ultimately outputting an optimized audio signal.

[0060] Specifically, the synthesis formula is ,in, represents the full-band reverse noise signal about t obtained after synthesis, and L represents the total number of sub-bands obtained by decomposition.

[0061] S5: Align the noise signal and the reverse noise signal based on the dynamic phase compensation order.

[0062] Among them, the dynamic phase compensation order refers to the number of historical data points used when aligning the noise signal and the reverse noise signal. It determines the number of past samples considered when phase aligning the noise signal and the reverse noise signal. The phase compensation order is usually dynamically adjusted according to the propagation delay of the noise and the characteristics of the audio signal to optimize the alignment effect of the signal. Based on the dynamic phase compensation order, the system aligns the noise signal and the reverse noise signal. By adjusting the phase compensation order, the system ensures the phase synchronization of the noise and reverse noise signals, thereby enhancing the noise cancellation effect. Dynamically adjusting the phase compensation order can effectively cope with the changes of different noise signals and optimize the noise reduction process.

[0063] In a possible implementation manner, the calculation formula for the dynamic phase compensation order is specifically:

[0064] in, represents the propagation delay of the noise signal obtained by the test, Indicates the frequency of wireless audio data collection. It indicates the phase adjustment accuracy related to the frequency of wireless audio data, and M indicates the dynamic phase compensation order.

[0065] Among them, the phase adjustment accuracy can be set to the reciprocal of the main frequency component in the wireless audio data. The compensation order indicates the number of historical data points used by the filter. The compensation order determines how many past samples the adaptive filter uses to calculate in order to achieve the best phase alignment. The dynamic phase compensation order is calculated to ensure the precise alignment of the noise signal and the reverse noise signal. By considering the noise propagation delay, audio acquisition frequency and phase adjustment accuracy, the phase compensation can be adjusted dynamically to optimize the signal synchronization effect. In this way, the noise can be offset more accurately, while avoiding audio distortion caused by phase misalignment, improving the noise reduction effect and sound quality.

[0066] In a possible implementation, S5 is specifically:

[0067] The noise signal and the reverse noise signal are aligned by an all-pass filter. The alignment formula is as follows:

[0068] in, represents the reverse noise signal at time t after alignment, represents the reverse noise signal when the phase compensation order is m, represents the phase compensation filter coefficient under m, It means to take the noise signal phase at time t and Phase The phase compensation filter coefficient when it takes the minimum value.

[0069] It should be noted that by aligning the phase of the noise signal and the reverse noise signal through the all-pass filter, it can effectively ensure that the reverse noise signal is in phase with the noise signal, thereby optimizing the noise cancellation effect. This process dynamically adjusts the phase compensation filter coefficient to match the phase of the reverse noise signal with the original noise signal, minimizes the noise interference in the audio signal, maintains the clarity and naturalness of the sound quality, and improves the noise reduction efficiency.

[0070] S6: synthesize the wireless audio data and the aligned reverse noise signal to obtain optimized wireless audio data.

[0071] It can be understood that the system synthesizes the original wireless audio data with the aligned reverse noise signal. Through this synthesis, the noise component in the original signal is effectively offset while retaining the effective part of the original audio, and finally the optimized wireless audio data is obtained. In this way, the optimized signal not only reduces the interference of noise, but also improves the audio quality.

[0072] In a possible implementation manner, S6 specifically includes: The wireless audio data and the aligned reverse noise signal are synthesized according to the timestamp to obtain optimized wireless audio data.

[0073] It can be understood that by synthesizing the original wireless audio data with the aligned reverse noise signal by timestamp, the noise component is effectively reduced while retaining the effective information of the audio, thereby improving the audio quality and ensuring the accuracy of noise reduction.

[0074] S7: Output optimized wireless audio data.

[0075] In the actual application process, first, audio data is collected through wireless devices (such as microphones or headphones). Then, the autoregressive prediction algorithm is used to determine whether there is noise. If noise is detected, the noise signal is extracted. Next, the reverse noise signal is generated through the sparse dictionary learning algorithm and the adaptive filter step size to offset the noise component. Then, the phase alignment of the noise signal and the reverse noise signal is adjusted by the dynamic phase compensation order to ensure the best noise reduction effect. Then, the optimized audio data is synthesized to remove noise and retain audio details, and finally the optimized wireless audio data is output. This process effectively improves the audio quality and noise reduction efficiency and reduces the impact of noise.

[0076] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least: In the embodiment of the present invention, the autoregressive prediction algorithm is first used to perform a pre-noise judgment on the collected wireless audio data. Noise reduction can only be performed when it is judged that there is noise. By analyzing the autocorrelation characteristics of the audio data, the presence of noise is accurately judged, and noise reduction is avoided in the absence of noise, thereby improving the noise reduction efficiency and audio quality, effectively avoiding unnecessary noise reduction processing, improving the calculation efficiency, reducing the audio loss caused by global noise reduction, and improving the accuracy of noise judgment. In the process of noise reduction, a sparse dictionary learning algorithm is combined, and a reverse noise signal for offsetting the noise signal is generated by a filter with an adaptive filtering step size. The sparse dictionary learning algorithm can effectively extract the noise component by learning the noise characteristics and performing sparse representation, and the adaptive filtering step size dynamically adjusts the filter according to the noise change, thereby achieving more accurate noise elimination and better audio quality. The noise signal and the reverse noise signal are aligned based on the dynamic phase compensation order, and then the wireless audio data and the aligned reverse noise signal are synthesized to obtain the optimized wireless audio data and output. It can accurately judge the noise, dynamically adjust the filter and extract the noise characteristics, thereby effectively improving the accuracy of noise elimination, avoiding unnecessary noise reduction processing, and improving the audio quality and processing efficiency.

[0077] Reference Manual Attached Figure 2 , showing a structural schematic diagram of a wireless audio sound effect processing system provided by the present invention.

[0078] The present invention further provides a wireless audio sound effect processing system 20, which is applied to the above-mentioned wireless audio sound effect processing method, comprising: Processor 201.

[0079] The memory 202 stores computer-readable instructions. When the computer-readable instructions are executed by the processor 201, the wireless audio effect processing method of the method embodiment is implemented.

[0080] The wireless audio sound effect processing system 20 provided by the present invention can execute the above-mentioned wireless audio sound effect processing method and achieve the same or similar technical effects. To avoid repetition, the present invention will not go into details.

[0081] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least: In the embodiment of the present invention, the autoregressive prediction algorithm is first used to perform a pre-noise judgment on the collected wireless audio data. Noise reduction can only be performed when it is determined that there is noise. By analyzing the autocorrelation characteristics of the audio data, the presence of noise is accurately judged to avoid noise reduction in the absence of noise, thereby improving the noise reduction efficiency and audio quality, effectively avoiding unnecessary noise reduction processing, improving calculation efficiency, reducing audio loss caused by global noise reduction, and improving the accuracy of noise judgment. In the process of noise reduction, a sparse dictionary learning algorithm is combined, and a filter with an adaptive filtering step size is used to generate a reverse noise signal for offsetting the noise signal. The sparse dictionary learning algorithm can effectively extract noise components by learning noise features and performing sparse representation, and the adaptive filtering step size dynamically adjusts the filter according to noise changes, thereby achieving more accurate noise elimination and better audio quality. The noise signal and the reverse noise signal are aligned based on the dynamic phase compensation order, and then the wireless audio data and the aligned reverse noise signal are synthesized to obtain optimized wireless audio data and output. It can accurately judge noise, dynamically adjust filters and extract noise features, thereby effectively improving the accuracy of noise elimination, avoiding unnecessary noise reduction processing, and improving audio quality and processing efficiency.

[0082] It should be understood that the processor in the embodiment of the present invention may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0083] It should also be understood that the memory in the embodiments of the present invention may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0084] The above embodiments may be implemented in whole or in part by software, hardware (such as circuits), firmware or any other combination thereof. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present invention is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium, or may be transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired means (such as infrared, wireless, microwave, etc.). The computer-readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server or data center that includes one or more available media sets. The available medium may be a magnetic medium (such as a floppy disk, a hard disk, a tape), an optical medium (such as a DVD), or a semiconductor medium. The semiconductor medium may be a solid-state hard disk.

[0085] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there may be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural. In addition, the character "" in this article generally indicates that the associated objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific understanding.

[0086] In the present invention, "at least one" means one or more, and "more" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.

[0087] It should be understood that in various embodiments of the present invention, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0088] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0089] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described equipment, devices and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0090] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0091] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0092] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0093] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program codes.

[0094] An embodiment of the present invention provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the wireless audio sound effect processing method as described in the method embodiment is implemented.

[0095] A computer-readable storage medium provided by the present invention can implement the steps and effects of the wireless audio sound effect processing method of the above method embodiment. To avoid repetition, the present invention will not go into details.

[0096] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least: In the embodiment of the present invention, the autoregressive prediction algorithm is first used to perform a pre-noise judgment on the collected wireless audio data. Noise reduction can only be performed when it is determined that there is noise. By analyzing the autocorrelation characteristics of the audio data, the presence of noise is accurately judged to avoid noise reduction in the absence of noise, thereby improving the noise reduction efficiency and audio quality, effectively avoiding unnecessary noise reduction processing, improving calculation efficiency, reducing audio loss caused by global noise reduction, and improving the accuracy of noise judgment. In the process of noise reduction, a sparse dictionary learning algorithm is combined, and a filter with an adaptive filtering step size is used to generate a reverse noise signal for offsetting the noise signal. The sparse dictionary learning algorithm can effectively extract noise components by learning noise features and performing sparse representation, and the adaptive filtering step size dynamically adjusts the filter according to noise changes, thereby achieving more accurate noise elimination and better audio quality. The noise signal and the reverse noise signal are aligned based on the dynamic phase compensation order, and then the wireless audio data and the aligned reverse noise signal are synthesized to obtain optimized wireless audio data and output. It can accurately judge noise, dynamically adjust filters and extract noise features, thereby effectively improving the accuracy of noise elimination, avoiding unnecessary noise reduction processing, and improving audio quality and processing efficiency.

[0097] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by any person skilled in the art within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

[0098] The following points need to be explained: (1) The drawings of the embodiments of the present invention only involve structures related to the embodiments of the present invention, and other structures can refer to the general design.

[0099] (2) For the sake of clarity, in the drawings used to describe the embodiments of the present invention, the thickness of layers or regions is exaggerated or reduced, that is, these drawings are not drawn according to the actual scale. It is understood that when an element such as a layer, film, region or substrate is referred to as being "on" or "under" another element, the element may be "directly" "on" or "under" the other element or there may be intermediate elements.

[0100] (3) In the absence of conflict, the embodiments of the present invention and the features therein may be combined with each other to obtain new embodiments.

[0101] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. The protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A wireless audio sound effect processing method, characterized in that the method include: S1: Collect wireless audio data; S2: Determine whether the wireless audio data has noise by using an autoregressive prediction algorithm. If yes, proceed to step S3; otherwise, mark the wireless audio data as optimized wireless audio data and proceed to step S7; S3: extracting a noise signal from the wireless audio data; S4: In combination with a sparse dictionary learning algorithm, a reverse noise signal for canceling the noise signal is generated through a filter with an adaptive filtering step size; S5: aligning the noise signal and the reverse noise signal based on a dynamic phase compensation order; S6: synthesizing the wireless audio data and the aligned reverse noise signal to obtain optimized wireless audio data; S7: Outputting the optimized wireless audio data.

2. The wireless audio effect processing method according to claim 1, characterized in that: The step of determining whether the wireless audio data has noise by using an autoregressive prediction algorithm in S2 specifically includes: S201: Frame the wireless audio data in combination with an autoregressive order related to the maximum noise length: ， Among them, N w represents the frame length, p represents the autoregressive order of the autoregressive prediction algorithm, N max Indicates the maximum noise length, N h Indicates the degree of overlap between frames; S202: Calculate the autocorrelation coefficient of each frame of wireless audio data obtained by framing at different lag orders and the noise variance related to the prediction error of the autoregressive prediction algorithm: ， in, represents the estimated value of the autocorrelation function of the wireless audio data under the lag coefficient τ for each frame of wireless audio data, N represents the total number of samples in the wireless audio data of the current frame, x k and x k-τ Respectively represent the kth wireless audio data value and the k-τth wireless audio data value in the wireless audio data of the current frame, a i Represents the autocorrelation coefficient of the wireless audio data of the current frame at the i-th order, i=1,2,p, represents the noise variance, e k represents the prediction error of the kth wireless audio data value in the wireless audio data of the current frame; S203: Calculate the detection signal difference between the actual value of the wireless audio data and the predicted value of the wireless audio data in combination with the calculated autocorrelation coefficient: ， in, Indicates the actual value of the wireless audio data of the current frame at time t, represents the wireless audio data value at lag i, represents the detection signal difference at time t; S204: judging whether there is noise in the wireless audio data by using the noise standard deviation according to the detection signal difference: ； Where I represents an exponential variable. When I=1, it means that the wireless audio data has noise. When I=0, it means that the wireless audio data has no noise. represents the noise standard deviation, Represents the noise filtering threshold.

3. The wireless audio effect processing method according to claim 2, characterized in that: The S3 is specifically: The detection signal difference is extracted as the noise signal.

4. The wireless audio effect processing method according to claim 1, characterized in that: The S4 specifically includes: S401: Decomposing the noise signal into multiple sub-frequency bands; S402: Perform sparse dictionary learning on each sub-band to obtain sparse coefficients corresponding to noise features: ， in, The noise signal is decomposed into sub-bands, express The corresponding dictionary matrix, express The corresponding sparse coefficient vector, represents the regularization parameter representing the sparsity constraint weight, represents the square of the L2 norm, represents the L1 norm, It means to take the value that minimizes the function and ; S403: Determine the adaptive filtering step size of the filter according to the sparse coefficient vector: ， in, represents the number of non-zero elements in the sparse coefficient vector, Z represents the number of dictionary atoms, and Respectively represent the maximum filter step size and the minimum filter step size, represents a constant that avoids zero denominator; S404: Determine the filter weight of each sub-band according to the error value between the sub-band correction value output by the filter and the actual value of the sub-band: ， in, Indicates sub-band The error value at time t is Indicates sub-band The actual value of the sub-band at time t, and Represents sub-bands The filter weights at time t and time t+1; S405: synthesizing each sub-frequency band obtained after filtering by the filter having the adaptive filtering step size and the filter weight to obtain the reverse noise signal.

5. The wireless audio effect processing method according to claim 4, characterized in that: When the noise change rate is greater than a preset noise change rate, the noise signal is determined to be transient noise; otherwise, the noise signal is determined to be steady-state noise; The calculation formula of the noise change rate is specifically: ， in, Indicates the sub-band at time t The noise change rate, and Represents the sub-bands at time t and time t-1 respectively , it is considered as transient noise.

6. The wireless audio effect processing method according to claim 1, characterized in that: The calculation formula of the dynamic phase compensation order is specifically: ， in, represents the propagation delay of the noise signal obtained by the test, Indicates the frequency of wireless audio data collection. It indicates the phase adjustment accuracy related to the frequency of wireless audio data, and M indicates the dynamic phase compensation order.

7. The wireless audio effect processing method according to claim 6, characterized in that: The S5 is specifically: The noise signal and the reverse noise signal are aligned by an all-pass filter, and the alignment formula is specifically: ， in, represents the reverse noise signal at time t after alignment, represents the reverse noise signal when the phase compensation order is m, represents the phase compensation filter coefficient under m, It means to take the noise signal phase at time t and Phase The phase compensation filter coefficient when it takes the minimum value.

8. The wireless audio effect processing method according to claim 1, characterized in that: The S6 is specifically: The wireless audio data and the aligned reverse noise signal are synthesized according to the timestamp to obtain the optimized wireless audio data.

9. A wireless audio sound effect processing system, characterized in that: include: processor; A memory having computer-readable instructions stored thereon, wherein when the computer-readable instructions are executed by the processor, the wireless audio sound effect processing method as claimed in any one of claims 1 to 8 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the wireless audio sound effect processing method as described in any one of claims 1 to 8 is implemented.

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