Wireless microphone system and mixing method

Abstract

The present application relates to the technical field of wireless microphone mixing, and discloses a wireless microphone system and a mixing method. The method collects audio signals through multiple wireless microphones; determines corresponding audio quality indicators based on the collected audio signals; determines whether to perform audio processing on the corresponding audio signals according to each audio quality indicator; when it is determined that audio processing is needed, processes the audio signals; determines optimal mixing parameters based on the processed audio signals; and adjusts the mixing output according to the optimal mixing parameters. The method can perform targeted processing according to the quality differences of different audio signals, optimize the quality of individual audio signals, determine optimal mixing parameters that can adapt to the actual situation of the current signals, thereby improving the quality of the mixing output, enhancing the stability of the mixing, reducing manual intervention, and being suitable for audio mixing requirements in multiple scenarios such as conferences and performances.

Description

Technical Field

[0001] This invention relates to the field of wireless microphone mixing technology, specifically to a wireless microphone system and mixing method. Background Technology

[0002] In the current field of audio transmission and processing, wireless microphone systems, with their flexibility and portability, are widely used in various scenarios such as conference presentations, stage performances, and broadcast television recordings. As application scenarios continue to diversify, a single wireless microphone can no longer meet the needs of multi-source audio acquisition; therefore, the collaborative operation of multiple wireless microphones is gradually becoming the mainstream. However, in the process of multiple wireless microphones simultaneously acquiring audio signals, the audio mixing stage faces many problems that urgently need to be solved.

[0003] Different wireless microphones operate in different environments. Some microphones may be located in areas with strong signal interference, resulting in a large amount of noise mixed in the acquired audio signal. Other microphones may be too close or too far from the sound source, leading to audio signal overload or weak volume. When these audio signals of varying quality are directly entered into the mixing process, they will cause problems such as uneven volume and noise superposition in the final mixed output, seriously affecting the listener's listening experience.

[0004] Most existing mixing methods use fixed mixing parameters without considering the quality differences between various audio signals. For example, if a noisy audio signal is directly mixed without targeted noise reduction, the signal-to-noise ratio of the entire mixing result will decrease. Conversely, if an audio signal with large volume fluctuations is not stabilized, the mixed output will exhibit inconsistent volume. This "one-size-fits-all" approach cannot be flexibly adjusted according to the actual conditions of the audio signal, making it difficult to guarantee the stability and high quality of the mixed output.

[0005] In scenarios where multiple microphones work together, audio signal transmission is subject to latency. Differences in the distance between different wireless microphones and the receiving device, as well as variations in interference along the signal transmission path, can all lead to variations in the arrival time of the audio signal at the receiving device. These audio signals with time differences are prone to phase shifts, sound overlap, and other problems during mixing, further degrading the quality of the mixed output. Furthermore, traditional mixing methods often rely on manual experience to adjust mixing parameters, which is not only cumbersome but also difficult to adapt to real-time changes in audio signal quality. When audio signal quality suddenly deteriorates, the mixing parameters cannot be adjusted in time, resulting in a consistently poor mixing effect. Summary of the Invention

[0006] The purpose of this invention is to provide a wireless microphone system and a mixing method to solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides a wireless microphone system and a mixing method, the method comprising:

[0008] Audio signals are acquired using multiple wireless microphones; audio quality indicators are determined based on the audio signals; audio processing is performed based on each audio quality indicator; when audio processing is performed, the audio signals are processed; optimal mixing parameters are determined based on the processed audio signals; and the mixing output is adjusted based on the optimal mixing parameters.

[0009] Preferably, the audio quality indicators include signal-to-noise ratio and signal uniformity.

[0010] When determining the audio quality index, for each wireless microphone, the audio signal is decomposed based on the voltage signal at multiple consecutive time points to generate decomposed components and residual components.

[0011] Based on the decomposed components and residual components, a corrected voltage signal at multiple consecutive time points is generated by noise reduction processing.

[0012] The audio quality index is determined based on the corrected voltage signals at multiple consecutive time points corresponding to each of the wireless microphones.

[0013] Preferably, the audio quality index is determined based on the corrected voltage signals at multiple consecutive time points corresponding to each of the wireless microphones, including:

[0014] For each wireless microphone, the corrected voltage signal of the wireless microphone at multiple consecutive time points is sampled to obtain the voltage signal of the wireless microphone at multiple sampling time points.

[0015] For each sampling time point, the single-point signal uniformity corresponding to the sampling time point is calculated based on the voltage signal of each wireless microphone at the sampling time point.

[0016] The audio quality index is determined based on the single-point signal uniformity corresponding to each sampling time point.

[0017] Preferably, determining whether to perform audio processing based on each of the audio quality indicators includes:

[0018] For any two wireless microphones, calculate the audio quality index difference between the two wireless microphones;

[0019] Calculate the audio quality index difference parameter based on the audio quality index difference between any two wireless microphones;

[0020] When the audio quality index difference parameter is less than the preset audio quality index difference parameter, and at least one of the wireless microphones has an audio quality index greater than the preset audio quality index, it is determined that audio processing should be performed.

[0021] Preferably, processing the audio signal includes performing audio processing operations in multiple processing modes;

[0022] Determining the optimal mixing parameters based on the processed audio signal includes:

[0023] For each of the processing modes, the relative positional relationship between noise feature points and multiple reference points is determined based on the audio signal processed under the processing mode;

[0024] Based on the relative positional relationship between the noise feature points corresponding to each processing mode and the plurality of reference points, a second relative positional relationship between the noise feature points and the plurality of reference points is determined;

[0025] The optimal mixing parameters are determined based on the second relative positional relationship between the noise feature points and the plurality of reference points.

[0026] Preferably, determining the optimal mixing parameters based on the second relative positional relationship between the noise feature points and the plurality of reference points includes:

[0027] Based on the second relative positional relationship between the noise feature points and the multiple reference points, multiple candidate mixing parameters are determined;

[0028] Obtain the battery temperature information of the wireless microphone at multiple consecutive time points;

[0029] Acquire ambient temperature information at multiple consecutive time points;

[0030] Based on the battery temperature information of the wireless microphone at multiple consecutive time points and the ambient temperature information at multiple consecutive time points, the multiple candidate mixing parameters are screened to determine the optimal mixing parameters.

[0031] Preferably, the method further includes temperature control of the wireless microphone based on battery temperature information of the wireless microphone at multiple consecutive time points, ambient temperature information at multiple consecutive time points, and the optimal mixing parameters.

[0032] Preferably, adjusting the mix output based on the optimal mixing parameters includes:

[0033] Based on historical mixing information, a preset mixing strategy is generated, wherein the preset mixing strategy includes preset mixing parameters for multiple time periods;

[0034] When the parameter difference between the optimal mixing parameter and the corresponding preset mixing parameter is greater than the preset difference, the mixing output and the preset mixing strategy are adjusted according to the optimal mixing parameter.

[0035] Preferably, when determining whether to perform audio processing based on the audio quality indicators, an audio interference risk value is calculated;

[0036] The audio interference risk value is obtained based on the initial state information, the current mixing path, and the current time external device state information.

[0037] When the audio interference risk value is abnormal, multiple mixing schemes are generated;

[0038] By combining environmental feature information, the cooperative interference value corresponding to the current time when the external device switches to the initial state is calculated, and the interference impact value of each mixing scheme on the mixing state is obtained.

[0039] The optimal mixing scheme is selected based on the interference impact value.

[0040] Preferably, after generating the best mixing scheme, the current mixing path in the database is updated in real time; mixing log information is generated based on the update results.

[0041] Compared with the prior art, the beneficial effects of the present invention are:

[0042] This wireless microphone mixing method can perform targeted processing based on the quality of different audio signals, effectively improving the overall quality of the mixed output. In practical applications, after acquiring audio signals through multiple wireless microphones, the corresponding audio quality indicators are first determined based on these audio signals. Then, based on each audio quality indicator, it is determined whether audio processing is required. This processing method can avoid unnecessary processing of high-quality audio signals, reducing the possibility of signal distortion, while ensuring that low-quality audio signals receive timely and appropriate processing. For example, when the noise index of an audio signal exceeds a reasonable range, it can be determined to perform noise reduction processing, thereby reducing the adverse impact of the audio signal on the overall mixing result; while audio signals with stable volume and low noise can directly enter the subsequent mixing stage, ensuring the original sound quality of the signal.

[0043] Once audio processing is determined to be necessary, processing the corresponding audio signals can further optimize the quality of individual audio signals, laying a solid foundation for subsequent mixing. Processed audio signals show improvements in noise, volume fluctuations, and frequency response, allowing them to blend better with other signals during mixing and reducing discrepancies caused by the quality of individual signals. For example, audio signals processed for volume stabilization will not exhibit fluctuating output volumes due to sudden increases or decreases in their own volume, thus improving the stability of the mixed output.

[0044] Determining optimal mixing parameters based on the processed audio signal ensures a high degree of match between the mixing parameters and the actual conditions of the current audio signal, avoiding the drawbacks of traditional fixed mixing parameters or reliance on manual experience for parameter adjustments. Since the quality of the processed audio signal has been optimized, the determined mixing parameters fully consider the characteristics of each audio signal, such as volume levels and frequency distributions, thus achieving more reasonable signal superposition and fusion. This method of determining optimal parameters based on actual signal quality can flexibly adapt to real-time changes in audio signal quality. When the audio signal quality changes due to environmental variations, the reprocessed audio signal will generate new optimal mixing parameters, ensuring that the mixed output is always in a good state.

[0045] Adjusting the mixing output based on optimal mixing parameters allows for a superior final mixing result in overall volume, timbre balance, and signal-to-noise ratio. The adjusted output avoids a decrease in overall signal-to-noise ratio due to excessive noise in some areas, and prevents some sounds from being masked due to uneven signal volume. In conference settings, this method ensures that each speaker's voice is clearly audible and maintains a stable volume, preventing one speaker from being ignored due to weakness or others from being overly loud. In stage performances, it allows for the perfect blending of different sound sources, such as singers' voices and instruments, creating a more immersive auditory experience and enhancing the audience's viewing experience. Furthermore, this method eliminates the need for frequent manual intervention in adjusting mixing parameters, reducing workload and increasing efficiency. This is particularly beneficial in scenarios requiring long-term audio acquisition and mixing, effectively reducing labor costs and ensuring continuous and stable mixing operations. Attached Figure Description

[0046] Figure 1 This is a schematic diagram illustrating the working principle of the wireless microphone system and mixing method described in this invention.

[0047] Figure 2 A flowchart of a method for mixing audio using a wireless microphone, including the determination of audio quality metrics;

[0048] Figure 3 A flowchart of a wireless microphone mixing method that includes the judgment of audio quality index differences;

[0049] Figure 4 A flowchart illustrating a method for determining optimal mixing parameters for wireless microphones using temperature-informed filtering. Detailed Implementation

[0050] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0051] Please see Figure 1 This invention provides a wireless microphone system and a mixing method, the method comprising:

[0052] The system comprises multiple wireless microphones, a signal processing unit, and a mixing output unit. The wireless microphones collect audio signals from the environment and transmit them to the signal processing unit. The signal processing unit calculates an audio quality index based on the received audio signals, which is used to evaluate the usability and quality of the audio signals. Based on the result of the audio quality index assessment, it determines whether to initiate the audio processing flow. When audio processing is deemed necessary, the signal processing unit performs specific processing operations on the audio signals, and the processed audio signals are used to determine the optimal mixing parameters. The mixing output unit adjusts the mixing output according to the optimal mixing parameters, achieving adaptive mixing control.

[0053] Example 1: See Figure 2In the actual operation of the wireless microphone system, multiple microphones were deployed in a medium-sized conference room to collect audio signals from multiple speakers. In the rectangular layout of the conference room, six omnidirectional condenser microphones were positioned in the center of the conference table, at the four corners, and in front of the speaker, forming a uniform sound field coverage network. These microphones continuously transmitted the raw voltage signals they collected to the central signal processing unit located in the equipment room via a 2.4GHz wireless transmission protocol. Time-division multiple access (TDMA) technology was used during transmission to avoid signal collisions. The signal processing unit adopted a multi-core DSP architecture, capable of processing multi-channel audio data in parallel. The received voltage signals were digitized with a 24-bit depth and a 48kHz sampling rate to form a continuous time-series voltage dataset. When the signal processing unit initiated the audio quality index calculation process, it preprocessed the raw voltage signals of each microphone channel. Preprocessing included DC offset correction and gain normalization to ensure that the signals of each channel had the same baseline level. The system then employs an adaptive window selection algorithm to dynamically adjust the analysis window size based on the signal's energy fluctuation characteristics. The baseline window is set to 1024 sampling points, corresponding to a time length of approximately 21 milliseconds, which effectively captures phoneme-level features in the speech signal. For the data from each channel, the system executes an improved empirical mode decomposition algorithm. This algorithm can handle non-stationary signal features and decomposes complex audio waveforms into multiple intrinsic mode function components and a residual component through an iterative sieving process.

[0054] During the decomposition process, the system introduces a termination condition based on Shannon entropy to ensure that the frequency modulation characteristics of each component conform to the strict definition of the intrinsic mode function. Each decomposed component carries fluctuation information of a specific time scale in the original signal. High-frequency components correspond to consonant and plosive features in speech, mid-frequency components carry vowel formant information, and low-frequency components contain fundamental frequency and room reverberation characteristics. After all decompositions are completed, the system performs noise reduction based on energy entropy: calculating the energy distribution probability of each component, establishing an energy entropy model, and identifying components with energy entropy values ​​exceeding an adaptive threshold as noise-dominant components and filtering them out. The remaining speech information-dominant components are phase-aligned and corrected, and then synthesized into a corrected voltage signal through a weighted reconstruction algorithm, with weighting coefficients dynamically allocated according to the signal-to-noise ratio level of each component.

[0055] Based on the corrected voltage signal, the system begins calculating the signal uniformity index. The sampling interval is dynamically adjusted according to the speech activity detection results, using denser sampling (5-millisecond interval) during active speech periods and sparser sampling (20-millisecond interval) during silent periods. Instantaneous voltage values ​​from all microphones are collected at each sampling time point, forming a spatial sampling matrix. The system not only calculates the arithmetic mean but also simultaneously calculates the geometric mean and median of the voltage values, forming a multi-dimensional reference benchmark. The deviation is calculated using an improved normalized mean absolute deviation algorithm, which is robust to outliers and emphasizes the contribution of microphones in the central region through a Gaussian weighting function.

[0056] The calculation of single-point uniformity values ​​incorporates a spatial correlation coefficient adjustment factor, taking into account the geometrical positional relationships between microphones. Signal differences between adjacent microphones are given smaller weights, while differences between diagonally positioned microphones receive larger weights; this design better reflects the physical characteristics of sound wave propagation. The single-point uniformity values ​​generated at each sampling time point are also time-smoothed, employing an exponentially weighted moving average algorithm to reduce the impact of instantaneous fluctuations. Throughout the analysis period, the system not only calculates the arithmetic mean but also simultaneously records the variance, skewness, and kurtosis of the uniformity sequence; these statistics are used to assess the stability of signal uniformity. For the final value of the signal uniformity index, the system uses a truncated mean calculation method, removing the highest and lowest 10% of values ​​before averaging to eliminate the influence of extreme values. Simultaneously, the signal-to-noise ratio (SNR) index is calculated using an improved frequency domain analysis method based on auditory masking effects. The system performs a 4096-point windowed Fourier transform on the corrected voltage signal, using a Blackman-Harris window function to reduce spectral leakage.

[0057] In spectral analysis, the system first establishes a noise spectrum estimate based on a Gaussian mixture model, dynamically tracking changes in environmental noise. Speech activity detection employs a multi-feature joint decision-making mechanism, combining features such as short-time energy, zero-crossing rate, and spectral centroid. The determination of speech energy bands is achieved by calculating the integral energy on the Mel frequency scale, which better aligns with human auditory perception characteristics. Noise band identification considers not only static spectral characteristics but also analyzes the temporal evolution pattern of the spectrum, effectively distinguishing between steady-state and non-steady-state noise. Signal-to-noise ratio (SNR) calculation uses a segmented frequency band weighting algorithm, assigning different weight coefficients to different frequency bands, with the 300Hz-3400Hz band, where the main speech energy is concentrated, receiving the highest weight. The final SNR index is the logarithmic average of multiple consecutive analysis frames, expressed in decibels (dB). The entire index calculation process employs a pipelined parallel architecture, with signal processing of all six microphone channels occurring simultaneously. The calculated SNR and signal uniformity indices, along with their time variability indices, are stored in the system's circular buffer.

[0058] The system also establishes an indicator quality assessment mechanism to perform confidence checks on the calculated indicator values. Confidence checks are based on signal analyzability indicators, including signal amplitude, background noise level, and spectral flatness. Low-confidence indicator values ​​are flagged and used with caution in subsequent processing. Through this comprehensive signal processing and analysis method, the system can comprehensively evaluate the audio acquisition quality of microphone arrays in a conference room environment, providing reliable data support for audio processing decisions. This multi-dimensional, adaptive evaluation system is particularly suitable for handling complex scenarios common in conference environments, such as multiple speakers alternating, positional changes, and background noise variations, laying a technical foundation for high-quality voice capture and transmission. Throughout the processing flow, the system monitors the allocation of computing resources in real time and dynamically adjusts the complexity of the analysis algorithm according to the processing load. When processor resources are scarce, the system automatically switches to a simplified calculation mode to maintain continuous monitoring of key indicators. This flexible computing architecture ensures stable performance under various operating conditions, meeting the needs of real-time audio processing. All calculation processes are timestamped and strictly synchronized with the audio data stream, providing accurate time alignment references for mixing control.

[0059] Example 2: See Figure 3 During the operation of the wireless microphone system, after the audio quality indicators are evaluated, the system enters a crucial decision-making phase: determining whether the currently acquired audio signal requires further processing. This system is deployed in an academic lecture hall that can accommodate 500 people, used for on-site sound reinforcement at important academic conferences. Four high-sensitivity UHF wireless microphones are positioned in the center of the main stage, on both sides, and in the front row of the audience. Each microphone is equipped with an independent shockproof bracket and windproof cover to minimize mechanical noise and airflow interference. The audio signals acquired by each microphone are transmitted to the back-end control system via digital frequency hopping technology. The system has a dedicated indicator evaluation buffer to temporarily store the real-time calculated signal-to-noise ratio and signal uniformity indicator data.

[0060] When the decision-making logic is initiated, the system first verifies the validity of the indicator data in the buffer. The verification process includes checking the continuity of the data's timestamps, the reasonableness of the numerical range, and the integrity of the channels. The verified data is then sent to the difference analysis module, which employs a multi-level comparison architecture to evaluate the quality differences between microphones. The system not only performs simple pairwise comparisons but also establishes a group difference model based on cluster analysis. For any two microphones, such as microphone A in the center of the lectern and microphone B on the right, the system calculates the absolute difference in their signal-to-noise ratio (SNR) and signal uniformity (SQU).

[0061] These absolute differences are fed into an adaptive weighting system. The weighting coefficients are not fixed but dynamically adjusted based on current environmental conditions and signal characteristics. In scenarios dominated by speech signals, the weighting coefficient for the signal-to-noise ratio difference automatically increases to 0.7, while the weighting coefficient for the signal uniformity difference is 0.3. When music signals or complex acoustic environments are detected, the weights of the two indicators tend to be balanced. The weighted difference results are also corrected for environmental factors, taking into account the effects of microphone position differences, sound source distance, and room acoustic characteristics. The final quality difference value for each microphone pair not only reflects the numerical difference but also includes directional information, recording which microphone's performance is superior.

[0062] The system calculates the difference values ​​for six microphone pairs, but instead of simply taking the maximum value, it employs a quartile-based statistical analysis method. It first eliminates obviously outlier differences, using the upper quartile as the representative difference parameter. This parameter reflects the quality difference levels between most microphones, preventing a single outlier from excessively influencing the overall judgment. Simultaneously, the system stores multiple sets of preset thresholds in its memory, categorized according to different meeting types, venue size, and ambient noise levels. The system automatically selects the threshold set that best matches the current scenario, which includes independent threshold parameters for signal-to-noise ratio and signal uniformity.

[0063] For individual microphone quality assessment, the system employs multi-dimensional standards. Each microphone's signal-to-noise ratio (SNR) must simultaneously meet both global and local threshold requirements. The global threshold is derived from historical high-quality recording data, while the local threshold considers the microphone's specific location and intended function within the array. For example, the SNR threshold for audience microphones might be slightly lower than that of the main stage microphone, but with higher signal uniformity requirements. The system also establishes a correlation check mechanism to verify the logical consistency between SNR and signal uniformity, preventing false indicators due to sensor malfunctions or transmission errors. The system's decision-making follows a priority logic, checking whether the audio quality indicator difference parameter is less than a preset threshold. This check has two levels: first, a quick preliminary judgment is made; if it is significantly lower than the threshold, it passes directly; for boundary cases close to the threshold, a detailed analysis process is initiated, considering time-series trends and spatial distribution patterns. Secondly, it checks whether at least one microphone meets the quality requirements. This check not only considers the absolute value but also analyzes the stability and persistence of the indicator. A single microphone must maintain compliance for three consecutive detection cycles to be considered a qualified signal source.

[0064] When both conditions are met, the system generates a quality confirmation signal, allowing the audio signal to directly enter the mixing stage. If either condition is not met, the system will not only start the audio processing program but also trigger a diagnostic process, recording detailed anomaly data for subsequent analysis. During the decision-making process, the system continuously monitors environmental conditions, and reassesses the correctness of the decision once a significant change in the acoustic environment is detected. In this academic lecture hall application scenario, the system's judgment mechanism demonstrates good adaptability. When all microphones are uniformly affected by the low-frequency noise of the central air conditioning, although the signal-to-noise ratio of each microphone decreases, the difference parameter remains at a low level due to the consistent degree of decrease, and the system determines that no special processing is required. When a microphone is subjected to wireless interference or low battery power, its indicators will deviate significantly from those of other microphones. The system can not only detect this anomaly but also preliminarily determine the fault type based on the difference pattern.

[0065] The entire decision-making process employs an incremental learning mechanism. The system records the results of each decision and the subsequent processing effects to optimize the setting of weight coefficients and thresholds. This dual judgment standard based on relative difference and absolute quality, combined with real-time environmental awareness and historical experience learning, enables the system to make more accurate processing decisions. It avoids overprocessing under acceptable quality conditions while promptly identifying and addressing genuine quality issues, ensuring the reliability and consistency of academic conference recordings. The system also includes a decision confidence assessment mechanism, assigning a confidence score to each judgment result. Decisions with low confidence trigger a manual review flag, alerting technical personnel to monitor the system's operational status. This design, while ensuring automated processing efficiency, provides additional human oversight for critical situations, ensuring the reliability of system decisions. Detailed logs are maintained for all decision-making processes, including the values ​​of each indicator, weight selection, threshold comparison, and final decision results, providing comprehensive data support for subsequent system optimization and fault diagnosis.

[0066] Example 3: See Figure 4 During the operation of the wireless microphone system, when the system determines that audio processing is required, it activates a multi-mode processing mechanism to optimize the audio signal quality. This system is used in a large conference room setting, equipped with six wireless microphones to capture audio from multi-party discussions. The processing unit first sends the acquired raw signal into three different processing mode channels: adaptive filtering mode primarily suppresses periodic noise, spectral subtraction noise reduction mode focuses on eliminating broadband noise, and dynamic range compression mode is used to balance signal amplitude fluctuations. Each processing mode operates independently, applying algorithms to its input signal to produce the corresponding processed signal.

[0067] After each processing mode completes its operation, the system needs to evaluate the processing effect of each mode to provide a basis for determining the optimal mixing parameters. This evaluation process is based on the localization and analysis of noise feature points. The system performs frequency domain analysis on the signal output by each processing mode, and locates specific frequency points where noise energy is relatively concentrated by identifying the characteristics of energy distribution in the spectrum. These are marked as noise feature points. At the same time, the system pre-stores multiple reference point data, which correspond to the typical center frequencies of common noise types, such as 50Hz power line interference, 100-200Hz ambient hum, or wireless interference in specific frequency bands. For each processing mode, the system calculates the Euclidean distance between its output noise feature points and each preset reference point. This distance value reflects the similarity between the noise condition after processing by this mode and the typical noise type. After obtaining the distance data between noise feature points and reference points for all processing modes, the system needs to generate a comprehensive evaluation index to guide parameter selection. This step is achieved by calculating the comprehensive weight of each reference point, the mathematical expression of which is as follows:

[0068]

[0069] in: Indicates the first The combined weight of each reference point Representing the Noise feature points in the processing mode to the first Euclidean distance between reference points This represents the total number of processing modes. The formula is designed so that reference points closer to the overall noise characteristic points receive higher weights, indicating that the system's noise characteristics are closer to the noise type represented by that reference point. Each reference point is pre-associated with a set of recommended mixing parameter settings, including gain, equalization curves, and noise thresholds. The system weights and sums these preset parameters according to their calculated weights to generate a set of candidate mixing parameter combinations. For example, if the low-frequency reference point has a higher weight, the generated candidate parameters will tend to strengthen low-frequency suppression.

[0070] After determining the candidate parameters, the system further incorporates environmental factors for screening. The system continuously monitors the battery temperature and ambient temperature data of each microphone, storing these temperature readings in time-series format. The system analyzes the correlation between battery temperature and ambient temperature, calculating the degree of matching between their changing trends over time. Candidate parameters that are inconsistent with temperature change trends are eliminated; for example, parameters that suggest significant gain adjustments when the temperature is stable, or parameters that suggest maintaining a constant value when the temperature fluctuates drastically. The final selected mixing parameters need to simultaneously meet the requirements of optimal noise reduction and compatibility with environmental conditions. This multi-mode processing and multi-factor decision-making mechanism enables the system to adapt to complex acoustic environments. In practical applications in conference rooms, multiple noise sources and different speech characteristics may coexist. Through parallel processing and multi-dimensional evaluation, the system can select the most suitable mixing parameters for the current environment, improving speech intelligibility while maintaining consistency with the equipment's operating status.

[0071] Example 4: In the actual operation of the wireless microphone system, after the optimal mixing parameters are determined, the system needs to implement corresponding temperature control strategies based on the equipment status and environmental conditions, and adjust the mixing output accordingly. This system is deployed in an outdoor performance setting, using eight wireless microphones for live audio acquisition, facing challenges such as large day-night temperature differences and continuous high-load operation of the equipment. The system establishes a temperature control mechanism. Based on real-time collected wireless microphone battery temperature data and ambient temperature data, combined with the determined optimal mixing parameters, the system calculates the required power adjustment and sampling rate adjustment for each microphone using a built-in temperature prediction model. This model considers the interrelationship between battery heat dissipation characteristics, environmental heat dissipation conditions, and audio processing load. For example, when the battery temperature of a microphone rises and the ambient temperature is high, the model will suggest appropriately reducing the microphone's transmit power and sampling rate to reduce equipment heat generation; conversely, in a lower-temperature environment, the system can allow higher power output without worrying about overheating. These adjustments are sent to each microphone device in real time via wireless control commands to achieve dynamic temperature management.

[0072] Regarding mixing output adjustments, the system maintains a database of preset mixing strategies, which stores preset mixing parameters divided by time periods based on historical data. These parameters are valid settings verified under similar past environmental conditions. The system compares the currently calculated optimal mixing parameters with the preset values ​​for the corresponding time period. When the parameter difference exceeds a predetermined threshold, the adjustment procedure is triggered. Table 1 shows the parameter comparison for three consecutive time periods on a certain afternoon.

[0073] Table 1: Record of Mixing Parameter Adjustments.

[0074] 14:00-15:00 12.5 15.2 2.7 flat High frequency boost Update gain and equalization 15:00-16:00 12.8 13.1 0.3 High frequency boost High frequency boost Keep the original settings 16:00-17:00 13.0 16.8 3.8 flat Intermediate frequency enhancement Update gain and equalization

[0075] Based on the comparison results, the system performs adjustments on two levels: on the one hand, it immediately adjusts the current mixing output according to the optimal mixing parameters, including modifying the gain settings of each channel, adjusting the equalizer parameters, and updating the noise threshold; on the other hand, it updates the preset mixing strategy, replacing the original preset values ​​with the current optimal parameters, so that the system can better adapt to similar environmental conditions.

[0076] In outdoor performance settings, this temperature-sensing mixing adjustment mechanism is particularly important. As sunlight intensity changes and ambient temperature fluctuates, microphone operating conditions and battery temperatures also change. By continuously monitoring temperature parameters, the system can proactively adjust equipment operating conditions, preventing performance degradation or equipment malfunction due to overheating. Simultaneously, dynamic adjustment of mixing parameters ensures optimal audio quality under various environmental conditions.

[0077] The entire control process forms a closed-loop system: temperature data influences the selection of mixing parameters, and the implementation of these parameters, in turn, affects the equipment's workload and temperature status. The system maintains its balance through continuous monitoring and adjustment. This multi-parameter collaborative control method enables the wireless microphone system to maintain stable performance under changing environmental conditions, ensuring both audio quality and extending the equipment's lifespan.

[0078] Example 5: In the complex application environment of wireless microphone systems, managing audio interference risk becomes a crucial aspect of ensuring audio quality. The system is deployed in a multimedia production studio, an environment with various electronic devices and high-density wireless signals, posing a potential threat to the normal operation of the wireless microphone. While determining whether audio processing is necessary based on audio quality indicators, the system simultaneously calculates an audio interference risk value. This comprehensive assessment value reflects the likelihood of audio quality degradation due to internal and external factors.

[0079] The calculation of the audio interference risk value is based on three dimensions of data input: the system's initial state information records the default parameter configuration and baseline performance indicators when the device starts up; the current mixing path details the real-time state parameters of the complete signal link from signal acquisition, processing to output; and the current external device state information covers the operating status of all peripheral devices in the studio that may generate electromagnetic interference, including wireless routers, video equipment, and lighting control systems. The system assigns different weight coefficients to each dimension. The initial state information has a relatively low weight because it represents the historical state; the current mixing path has the highest weight because it directly reflects the real-time signal quality; and the external device state has a moderate weight, reflecting the potential impact of environmental factors. These weighted data are fused and calculated through an algorithm model to ultimately generate a quantified audio interference risk value.

[0080] When the calculated audio interference risk value exceeds the system's preset safety threshold, the system determines that it is in an abnormal risk state and requires immediate countermeasures. The system automatically generates multiple alternative mixing schemes, which address potential interference issues from different angles. These schemes include adjusting the sampling rate to avoid specific interfering frequency bands, modifying the bandwidth to reduce the affected spectrum, activating backup wireless channels to avoid congested frequency bands, and adjusting signal coding parameters to enhance anti-interference capabilities. Each scheme includes specific parameter settings and expected performance characteristics.

[0081] When evaluating these mixing schemes, the system incorporates environmental characteristic information as a crucial reference. This information includes background noise spectrum characteristics and electromagnetic interference intensity distribution maps collected by the environmental monitoring unit. The system simulates and calculates the potential cooperative interference value if all external devices were reset to their initial states. This value reflects the additional interference effects that abrupt changes in device states might cause. Based on this cooperative interference value, the system further calculates the interference impact of each mixing scheme on the current mixing state. This value quantifies the degree of signal quality change that the scheme implementation might cause, including a comprehensive assessment of both positive and negative impacts.

[0082] The system compares and analyzes the interference impact values ​​of all mixing schemes, selecting the scheme with the lowest value as the optimal execution scheme. This selection criterion ensures that the system's countermeasures have minimal impact on the existing mixing state while effectively reducing interference risks. In practical applications in multimedia studios, this selection process needs to comprehensively consider multiple factors, such as program recording requirements, equipment operating status, and real-time environmental changes. After determining the optimal mixing scheme, the system immediately executes it and updates the current mixing path. The update operation includes recording the new path parameter configuration, scheme execution timestamp, and environmental state snapshot. This updated data is written to the system database in real time, ensuring that the system always retains the latest operational status information. Based on these update results, the system automatically generates detailed mixing log information. These logs not only include records of changes in technical parameters but also include state comparison data before and after scheme execution and environmental characteristic descriptions.

[0083] The audio interference risk management process forms a complete closed-loop system, with each step closely linked from risk value calculation and response implementation to system updates and log recording. In complex electromagnetic environments such as multimedia production studios, this dynamic risk management mechanism effectively addresses various unexpected interference situations, ensuring the stability of audio acquisition and processing. Through continuous learning and accumulation of processing experience, the system continuously improves the accuracy and efficiency of interference identification and response, providing a reliable guarantee for high-quality audio production. This mechanism, based on real-time risk assessment and dynamic response, reflects the direction of intelligent development in modern wireless audio systems, enabling the system to maintain stable performance in complex and ever-changing environments.

[0084] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0085] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A wireless microphone mixing method, applied to a wireless microphone system, characterized in that, Includes the following steps: Audio signals are acquired using multiple wireless microphones; audio quality indicators are determined based on the audio signals; and audio processing is performed based on each audio quality indicator. When it is determined that audio processing is to be performed, the audio signal is processed; the optimal mixing parameters are determined based on the processed audio signal; and the mixing output is adjusted based on the optimal mixing parameters. The audio quality metrics include signal-to-noise ratio and signal uniformity. When determining the audio quality index, for each wireless microphone, for the voltage signal corresponding to each wireless microphone, the voltage signal of the audio signal at multiple consecutive time points is decomposed, and the complex audio waveform is decomposed into multiple intrinsic mode function components and a residual component through an iterative sieving process. By denoising the intrinsic mode function components and a residual component, a corrected voltage signal is generated at multiple consecutive time points. The audio quality index is determined based on the corrected voltage signal at multiple consecutive time points corresponding to each of the wireless microphones; The audio quality index is determined based on the corrected voltage signal at multiple consecutive time points corresponding to each of the wireless microphones, including: For each wireless microphone, the corrected voltage signal of the wireless microphone at multiple consecutive time points is sampled to obtain the voltage signal of the wireless microphone at multiple sampling time points. For each sampling time point, the single-point signal uniformity corresponding to the sampling time point is calculated based on the voltage signal of each wireless microphone at the sampling time point. The audio quality index is determined based on the single-point signal uniformity corresponding to each of the sampling time points; The signal-to-noise ratio (SNR) index is calculated based on the corrected voltage signal using an improved frequency domain analysis method based on auditory masking effect. Specifically, it includes: performing a windowed Fourier transform on the corrected voltage signal, establishing a noise spectrum estimation based on a Gaussian mixture model to dynamically track environmental noise, detecting speech activity and identifying noise frequency bands, and calculating the SNR using a segmented frequency band weighting algorithm. Finally, the logarithmic average of multiple consecutive analysis frames is taken and expressed in decibels. Processing the audio signal includes performing audio processing operations in multiple processing modes; Determining the optimal mixing parameters based on the processed audio signal includes: For each of the processing modes, the relative positional relationship between noise feature points and multiple reference points is determined based on the audio signal processed under the processing mode; Based on the relative positional relationship between the noise feature points corresponding to each processing mode and the plurality of reference points, a second relative positional relationship between the noise feature points and the plurality of reference points is determined; The optimal mixing parameters are determined based on the second relative positional relationship between the noise feature points and the plurality of reference points; Based on the second relative positional relationship between the noise feature points and the plurality of reference points, the optimal mixing parameters are determined, including: Based on the second relative positional relationship between the noise feature points and the multiple reference points, multiple candidate mixing parameters are determined; Obtain the battery temperature information of the wireless microphone at multiple consecutive time points; Obtain ambient temperature information at multiple consecutive time points; Based on the battery temperature information of the wireless microphone at multiple consecutive time points and the ambient temperature information at multiple consecutive time points, the multiple candidate mixing parameters are screened to determine the optimal mixing parameters.

2. The wireless microphone mixing method according to claim 1, characterized in that, Determining whether to perform audio processing based on each of the aforementioned audio quality metrics includes: For any two wireless microphones, calculate the audio quality index difference between the two wireless microphones; Calculate the audio quality index difference parameter based on the audio quality index difference between any two wireless microphones; When the audio quality index difference parameter is less than the preset audio quality index difference parameter, and at least one of the wireless microphones has an audio quality index greater than the preset audio quality index, it is determined that audio processing should be performed.

3. The wireless microphone mixing method according to claim 1, characterized in that, It also includes temperature control of the wireless microphone based on battery temperature information of the wireless microphone at multiple consecutive time points, ambient temperature information at multiple consecutive time points, and the optimal mixing parameters.

4. The wireless microphone mixing method according to claim 3, characterized in that, Adjusting the mix output based on the optimal mixing parameters includes: Based on historical mixing information, a preset mixing strategy is generated, wherein the preset mixing strategy includes preset mixing parameters for multiple time periods; When the parameter difference between the optimal mixing parameter and the corresponding preset mixing parameter is greater than the preset difference, the mixing output and the preset mixing strategy are adjusted according to the optimal mixing parameter.

5. The wireless microphone mixing method according to claim 1, characterized in that, When determining whether to perform audio processing based on the aforementioned audio quality indicators, calculate the audio interference risk value. The calculation of the audio interference risk value is based on the initial state information, the current mixing path, and the current time external device state information. When the audio interference risk value is abnormal, multiple mixing schemes are generated; By combining environmental feature information, the cooperative interference value corresponding to the current time when the external device switches to the initial state is calculated, and the interference impact value of each mixing scheme on the mixing state is obtained. The optimal mixing scheme is selected based on the interference impact value.

6. The wireless microphone mixing method according to claim 5, characterized in that, After generating the optimal mixing scheme, the current mixing path in the database is updated in real time; mixing log information is generated based on the update results.

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