Automatic debugging method, device and equipment for multimedia sound console and medium

Abstract

The invention relates to an automatic debugging method, device and equipment for a multimedia sound console and a medium, and the method comprises the steps: obtaining sound field space data, matching a pre-cached basic cloud template according to the sound field space data, obtaining an input audio signal of multiple sound channels, and transmitting the input audio signal to the multimedia sound console; determining a spectrum characteristic and a dynamic range characteristic of the input audio signal; carrying out equalization analysis on the spectrum features, and determining a corresponding equalization parameter set; determining a corresponding gain parameter set according to the dynamic range characteristics; according to the reverberation parameter, the equalization parameter set, the gain parameter set and the filtering parameter set, a corresponding local cache template is established, a cloud template matching technology based on sound field space data is introduced, the limitation that traditional tuning depends on fixed parameters or manual adjustment is overcome, the automation degree of tuning is improved, manual intervention is reduced, and the tuning efficiency is improved. And meanwhile, the stability of audio output quality is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of multimedia debugging, and in particular to a method, device, equipment and medium for automatic debugging of a multimedia mixing console. Background Art

[0002] Currently, multimedia mixing consoles are widely used in performances, recordings, and conferences to adjust and optimize multi-channel audio. However, existing technologies still have the following problems in the automatic tuning process: First, traditional tuning methods usually rely on fixed equalization, gain, and reverberation parameters, which are difficult to adapt to different sound field environments, resulting in unstable sound quality. Secondly, when adjusting gain and equalization parameters, existing systems usually rely on manual debugging or simple preset templates, and lack the ability to adaptively optimize the spectral characteristics and dynamic range of the input audio signal. As a result, the tuning process relies on the operator's experience and cannot dynamically optimize parameters according to real-time audio characteristics, thus affecting the consistency and adaptability of sound quality. Summary of the Invention

[0003] In order to solve the problem that traditional multimedia mixing consoles use manual debugging or simple preset templates and lack adaptive optimization capabilities, the present application provides a method, device, equipment and medium for automatic debugging of multimedia mixing consoles.

[0004] A multimedia mixing console automatic debugging method, a multimedia mixing console automatic debugging method comprising: Acquire sound field spatial data and match pre-cached basic cloud templates according to the sound field spatial data. The basic cloud templates include at least preset equalization standards, gain control strategies, and reverberation parameters. Acquire a multi-channel input audio signal and determine the frequency spectrum characteristics and dynamic range characteristics of the input audio signal; Based on the preset equalization standard, the spectrum characteristics are analyzed and the corresponding equalization parameter set is determined; Based on the gain control strategy, a corresponding gain compensation amount is calculated according to the dynamic range characteristics, and a corresponding gain parameter set is determined according to the gain compensation amount; Performing a feedback noise detection operation on the input audio signal, and if it is determined that a howling frequency component exists, determining a corresponding filter parameter set according to the howling frequency component; Determine whether the basic cloud template needs to be updated. If so, establish a corresponding local cache template based on the reverberation parameters, equalization parameter set, gain parameter set, and filter parameter set. If not, determine that the basic cloud template is the local cache template.

[0005] By adopting the above technical solution and introducing cloud-based template matching technology based on sound field spatial data, the limitations of traditional tuning that relies on fixed parameters or manual adjustments are overcome. The system first obtains the sound field spatial data and automatically matches the basic cloud-based template that best suits the current environment based on the data, so that the equalization, gain control and reverberation parameters can match the actual application scenario, improving the environmental adaptability of the tuning. In terms of audio signal processing, by analyzing the spectral characteristics and dynamic range characteristics of the input audio signal, the equalization parameters and gain parameters are adaptively calculated, so that the volume and timbre between different channels can be dynamically balanced, avoiding the sound quality distortion or lack of layering caused by fixed parameters in traditional methods. In addition, in order to solve the howling problem, the system integrates a feedback noise detection mechanism. When the howling frequency is detected, it can calculate the appropriate set of filter parameters in real time and adjust the working state of the filter to accurately suppress the howling without excessively weakening other frequency components, ensuring clear and natural sound quality. Finally, based on the currently calculated parameters, the system determines whether the cloud-based template needs to be updated. If the fit is insufficient, a local cache template is created based on the latest reverberation, equalization, gain, and filter parameters to optimize matching accuracy and tuning efficiency for subsequent use. This approach not only improves the automation of tuning and reduces manual intervention, but also ensures the stability of audio output quality, allowing the mixer to better adapt to complex and changing application environments.

[0006] Preferably, the step of matching the pre-cached basic cloud template according to the sound field spatial data includes: Extracting required parameters of the sound field space data, the required parameters at least including room volume, reverberation time and background noise; Calling the locally cached matching model, which has multiple threshold intervals for each demand parameter; Substitute each requirement parameter into the matching model to generate a corresponding sub-code, and arrange each sub-code in sequence according to the arrangement rules of the matching model to generate a corresponding matching code; Match the corresponding basic cloud template according to the matching code.

[0007] By adopting the above technical solution, accurate matching analysis can be performed based on multiple key parameters such as room volume, reverberation time and background noise, so that the system can efficiently match the template that best suits the current sound field environment based on the existing data model, thereby improving the accuracy of template selection and avoiding the problem of insufficient applicability of traditional fixed template solutions in different sound field environments.

[0008] Preferably, the step of performing equalization analysis on the spectrum characteristics based on a preset equalization standard and determining a corresponding equalization parameter set includes: Based on the preset equalization standard, the spectrum characteristics are divided into multiple preset frequency bands, and the amplitude value and phase characteristics of each preset frequency band are calculated; According to the amplitude value, calculate the corresponding adjustment amount; According to the phase characteristics, the corresponding phase compensation parameters are calculated; The adjustment amount and phase compensation parameters are integrated to determine a corresponding equalization parameter set.

[0009] By adopting the above technical solution, it is possible to accurately divide the spectrum characteristics and perform equalization analysis in combination with the amplitude value and phase characteristics. This allows the equalization adjustment to not only consider amplitude compensation, but also compensate for the phase distortion caused by the equalization processing, thereby improving the equalization adjustment accuracy of the audio signal, ensuring the naturalness and consistency of the sound quality, and avoiding the spectrum imbalance or phase offset problems that may be caused by traditional equalization adjustment methods.

[0010] Preferably, based on the gain control strategy, the corresponding gain compensation amount is calculated according to the dynamic range characteristics, and the step of determining the corresponding gain parameter set according to the gain compensation amount includes: According to the gain control strategy, the corresponding extraction rules, target amplitude value and gain adjustment threshold are determined; Extracting an average amplitude value from a dynamic range feature based on an extraction rule, wherein the dynamic range feature is a statistical result of a maximum amplitude value, a minimum amplitude value, an average amplitude value, and a signal crest factor of an input audio signal; Calculate the gain compensation amount between the average amplitude value and the target amplitude value; According to the gain adjustment threshold, the gain compensation amount is subjected to amplitude constraint processing to generate a corresponding gain parameter set.

[0011] By adopting the above technical solution, the gain compensation amount can be calculated based on multiple key parameters in the dynamic range characteristics, and the amplitude constraint can be performed in combination with the gain adjustment threshold. This allows the gain adjustment to meet the volume consistency requirements while avoiding clipping distortion or excessive audio dynamic range compression caused by excessive gain adjustment, thereby improving the stability and sound quality fidelity of the audio signal.

[0012] Preferably, the step of determining the corresponding filter parameter set according to the howling frequency component includes: Performing spectrum analysis on the howling frequency component to determine the corresponding center frequency and bandwidth; Determining initial filter parameters for the notch filter based on the center frequency and bandwidth; Perform dynamic adaptive adjustment on the initial filtering parameters to generate a corresponding filtering parameter set.

[0013] By adopting the above technical solution, the center frequency and bandwidth of the howling frequency can be accurately analyzed, and the parameters of the notch filter can be adjusted based on real-time spectrum changes. The filter adjustment can effectively suppress howling without causing additional damage to non-howling frequency bands, thereby improving the accuracy of howling suppression and avoiding the sound quality loss or audio attenuation problems caused by traditional fixed notch filtering solutions.

[0014] Preferably, the step of establishing a corresponding local cache template according to the reverberation parameter, the equalization parameter set, the gain parameter set, and the filter parameter set includes: Performing normalization processing on the reverberation parameters, the equalization parameter set, the gain parameter set, and the filter parameter set to generate corresponding template parameter sets; Generate a multi-dimensional parameter mapping table based on the template parameter set; Perform hierarchical optimization processing on the multi-dimensional parameter mapping table to generate a corresponding local cache template.

[0015] By adopting the above technical solution, the center frequency and bandwidth of the howling frequency can be accurately analyzed, and the parameters of the notch filter can be adjusted based on real-time spectrum changes. The filter adjustment can effectively suppress howling without causing additional damage to non-howling frequency bands, thereby improving the accuracy of howling suppression and avoiding the sound quality loss or audio attenuation problems caused by traditional fixed notch filtering solutions.

[0016] In summary, this application includes at least one of the following beneficial technical effects: By introducing cloud-based template matching technology based on spatial sound field data, the system overcomes the limitations of traditional tuning, which relies on fixed parameters or manual adjustments. The system first acquires spatial sound field data and automatically matches a basic cloud-based template that best fits the current environment. This ensures that equalization, gain control, and reverberation parameters are tailored to the actual application scenario, improving the tuning's adaptability to specific environments. In terms of audio signal processing, the system adaptively calculates equalization and gain parameters by analyzing the spectral and dynamic range characteristics of the input audio signal. This dynamically balances the volume and timbre of different channels, avoiding the distortion and lack of layering that can result from fixed parameters in traditional methods. Furthermore, to address howling, the system integrates a feedback noise detection mechanism. When a howling frequency is detected, it calculates the appropriate filter parameter set in real time and adjusts the filter's operating state to accurately suppress the howling without excessively attenuating other frequency components, ensuring clear and natural sound quality. Finally, based on the currently calculated parameters, the system determines whether the cloud-based template needs to be updated. If the fit is insufficient, a local cache template is created based on the latest reverberation, equalization, gain, and filter parameters to optimize matching accuracy and tuning efficiency for subsequent use. This method not only improves the degree of automation of tuning and reduces manual intervention, but also ensures the stability of audio output quality, allowing the mixing console to better adapt to complex and changing application environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is a flow chart of a method for automatically debugging a multimedia mixing console in one embodiment of the present application.

[0018] Figure 2 This is a flowchart for implementing step S10 in a method for automatically debugging a multimedia mixing console according to an embodiment of the present application; Figure 3 This is a flowchart for implementing step S30 in a method for automatically debugging a multimedia mixing console according to an embodiment of the present application; Figure 4 This is a flowchart for implementing step S40 in a method for automatically debugging a multimedia mixing console according to an embodiment of the present application; Figure 5 This is a flowchart for implementing step S50 in a method for automatically debugging a multimedia mixing console according to an embodiment of the present application; Figure 6 This is a flowchart for implementing step S60 in a method for automatically debugging a multimedia mixing console according to an embodiment of the present application; Figure 7 This is a principle block diagram of an automatic debugging device for a multimedia mixing console in one embodiment of the present application; Figure 8 It is a schematic diagram of a device in one embodiment of the present application. DETAILED DESCRIPTION

[0019] The present application is further described in detail below with reference to the accompanying drawings.

[0020] In one embodiment, if Figure 1 As shown, this application discloses a multimedia mixer automatic debugging method, which is applied to the Hippo DM08X USB sound card digital mixer. The multimedia mixer automatic debugging method includes: S10. Acquire sound field spatial data and match it to a pre-cached basic cloud template based on the sound field spatial data. The basic cloud template includes at least preset equalization standards, gain control strategies, and reverberation parameters. In this embodiment, the sound field spatial data refers to a set of parameters used to characterize the physical and acoustic characteristics of the current acoustic environment, including but not limited to room volume, reverberation time, background noise level, and the distance between the sound source and the pickup device. This data can be obtained through the mixer's built-in environmental sensors, measurement microphone arrays, or external databases. For example, in a large, empty concert hall, the reverberation time is long and the background noise is low. The sound field spatial data will reflect this characteristic and guide the system to select a cloud template suitable for large, high-reverberation environments. In a small conference room, the system may select a template with low reverberation compensation to ensure vocal clarity. The process of matching the basic cloud template is based on the calculation of the compatibility between the sound field spatial data and the pre-stored tuning templates in the cloud. A classification algorithm or neural network model is used for matching, so that the selected template can optimize the audio performance in the current sound field environment, thereby reducing tuning time and improving the consistency and stability of sound quality.

[0021] S20. Obtain a multi-channel input audio signal and determine its spectral characteristics and dynamic range characteristics. In this embodiment, a multi-channel input audio signal refers to a collection of audio signals input to the mixing console from different sound sources (such as vocals, musical instruments, and ambient sounds) via multiple independent audio channels. Spectral characteristics refer to the energy distribution of the input audio signal at each frequency, typically obtained through Fast Fourier Transform (FFT) or Short-Time Fourier Transform (STFT) analysis. They are used to identify the frequency components of each sound source, such as low-frequency drum beats, mid-frequency vocals, or high-frequency background noise. Dynamic range characteristics refer to the amplitude variation of the audio signal over time, including parameters such as maximum amplitude, minimum amplitude, average amplitude, and instantaneous dynamic range. For example, in a concert, drums typically have a larger dynamic range, while vocals have a smaller dynamic range. By analyzing these characteristics, the system can identify the signal characteristics of each channel, providing data support for subsequent processing steps such as equalization and gain adjustment, ensuring the clarity, layering, and volume balance of the audio signal.

[0022] S30. Based on a preset equalization standard, the spectral characteristics are analyzed for equalization and a corresponding equalization parameter set is determined. In this embodiment, the preset equalization standard refers to a set of adjustment schemes designed to optimize frequency response for different audio scenarios and sound source types, typically including multiple preset frequency bands and their target gain values. Equalization analysis analyzes the energy distribution of each frequency band based on the spectral characteristics of the input audio signal to identify potential audio imbalances, such as excessive or weak frequencies in certain frequency bands. For example, in a musical performance, the mid- and high-frequency guitar may be too prominent, while the low-frequency bass is weak. The system can detect this imbalance and compensate for it based on the equalization standard. The equalization parameter set includes gain adjustment and phase compensation parameters for each frequency band, ensuring that the adjusted audio signal maintains its original timbre while achieving better sound quality balance and avoiding overly sharp or dull sounds. The equalization adjustment process can utilize adaptive filters or finite impulse response (FIR) filters to achieve more precise adjustments in different frequency bands, thereby optimizing the final output audio quality.

[0023] S40. Based on the gain control strategy, the corresponding gain compensation amount is calculated according to the dynamic range characteristics, and the corresponding gain parameter set is determined according to the gain compensation amount. In this embodiment, the gain control strategy is an optimization scheme for dynamically adjusting the volume based on the input signal characteristics, ensuring that the volume is balanced between different channels while preventing clipping distortion caused by excessive volume. The dynamic range characteristics include the instantaneous amplitude, average amplitude, and peak amplitude of the audio signal. When calculating the gain compensation amount, the system compares the dynamic range characteristics of the input signal with the target amplitude value to determine whether the gain needs to be increased or decreased. For example, in a broadcast or conference scenario, if a speaker's voice is low and others' voices are high, the system will detect that the amplitude value of the channel is lower than the target amplitude and increase the gain accordingly to make it reach a volume level similar to that of other channels. The gain parameter set includes the gain adjustment value and its change rate to ensure a smooth transition of the gain adjustment and avoid abrupt volume changes, thereby improving auditory comfort and overall volume consistency.

[0024] S50: Perform feedback noise detection on the input audio signal. If a howling frequency component is detected, a corresponding filter parameter set is determined based on the howling frequency component. In this embodiment, feedback noise detection refers to analyzing the spectral changes of the audio signal to determine whether there is acoustic feedback caused by the speaker signal returning to the microphone, which is usually manifested as a narrowband, high-amplitude, sharp howling sound. During the detection process, the system performs adaptive spectrum analysis on the input audio signal to identify specific frequency components that may cause howling. When a howling frequency is detected, the system calculates the corresponding filter parameter set, including the center frequency, bandwidth, and attenuation depth of the notch filter. For example, in a small conference room where the microphone and speaker are close together, the system may detect feedback noise in the 2kHz to 4kHz range and automatically configure a notch filter to reduce the gain in this frequency band, thereby suppressing the howling without affecting the overall sound quality. By dynamically adjusting the filter parameters, the system can suppress howling in real time while maintaining the naturalness of other frequency components, ensuring the clarity and audibility of the voice or music signal.

[0025] S60: Determine whether the base cloud template needs to be updated. If so, a corresponding local cache template is established based on the reverberation parameters, equalization parameter set, gain parameter set, and filter parameter set. If not, the base cloud template is determined to be the local cache template. In this embodiment, the update of the base cloud template is determined based on the compatibility calculation between the parameters of the current sound field environment and the cloud template. The system will evaluate whether the current reverberation parameters, equalization parameter set, gain parameter set, and filter parameter set deviate significantly from the preset template. For example, at a new performance venue, the actual measured reverberation time is more than 0.5 seconds longer than the preset value of the cloud template. The equalization parameters need to be significantly adjusted, and the gain and filter strategies also need to be reconfigured. At this time, the system will consider the original template inappropriate for the current scenario and trigger an update mechanism to regenerate the local cache template. The local cache template stores the optimized parameter set and can be directly called in subsequent similar scenarios, reducing the cloud computing burden and improving the tuning response speed, thereby enhancing the intelligence and adaptability of the system.

[0026] In summary, by introducing cloud-based template matching technology based on sound field spatial data, the limitations of traditional tuning that relies on fixed parameters or manual adjustments are overcome. The system first obtains the sound field spatial data, and automatically matches the basic cloud template that best suits the current environment based on the data, so that the equalization, gain control and reverberation parameters can match the actual application scenario, thereby improving the environmental adaptability of the tuning. In terms of audio signal processing, by analyzing the spectral characteristics and dynamic range characteristics of the input audio signal, the equalization parameters and gain parameters are adaptively calculated, so that the volume and timbre between different channels can be dynamically balanced, avoiding the sound quality distortion or lack of layering caused by fixed parameters in traditional methods. In addition, in order to solve the howling problem, the system integrates a feedback noise detection mechanism. When the howling frequency is detected, it can calculate the appropriate set of filter parameters in real time and adjust the working state of the filter to accurately suppress the howling without excessively weakening other frequency components, ensuring clear and natural sound quality. Finally, based on the currently calculated parameters, the system determines whether the cloud-based template needs to be updated. If the fit is insufficient, a local cache template is created based on the latest reverberation, equalization, gain, and filter parameters to optimize matching accuracy and tuning efficiency for subsequent use. This approach not only improves the automation of tuning and reduces manual intervention, but also ensures the stability of audio output quality, allowing the mixer to better adapt to complex and changing application environments.

[0027] In one embodiment, if Figure 2 As shown, in step S10, i.e., the step of matching the pre-cached basic cloud template according to the sound field spatial data, the following steps are included: S101. Extract required parameters for sound field spatial data. The required parameters include at least room volume, reverberation time, and background noise. In this embodiment, the required parameters for sound field spatial data refer to key physical parameters used to characterize the current acoustic environment, which can influence audio propagation characteristics and the final tuning effect. Room volume is typically calculated from the room's length, width, and height. It determines the sound propagation path in the space and the diffusion of sound energy. For example, a smaller room may cause sound to reflect back to the microphone in a shorter time, while a larger room may cause sound to decay more quickly or produce longer reverberation. Reverberation time indicates the length of time it takes for sound to reflect and gradually decay in space. It is typically determined by measuring the time it takes for sound energy to drop by 60 dB. For example, in a recording studio, reverberation time is typically short to ensure sound clarity, while in venues such as concert halls or churches, reverberation time is longer to create a more spatial sound effect. Background noise refers to the noise level in the environment, typically generated by ambient sounds such as air conditioning, fans, and conversations. It can affect microphone pickup quality. If background noise is excessive, the system may need to activate noise reduction algorithms or adjust microphone gain to ensure clarity of the target audio signal.

[0028] S102. Calling a locally cached matching model, which has multiple threshold intervals for each required parameter. In this embodiment, the locally cached matching model is a database or calculation module for analyzing sound field spatial data. The model has preset typical parameter ranges for different sound field environments and is divided into multiple threshold intervals to quickly classify and match the appropriate basic cloud template. The threshold intervals of the matching model are trained based on a large amount of audio environment data. For example, room volume may be divided into small (less than 50m³), medium (50-200m³), and large (over 200m³); reverberation time may be divided into short reverberation (less than 0.5 seconds), medium reverberation (0.5-1.5 seconds), and long reverberation (over 1.5 seconds); and background noise may be divided into quiet environment (less than 30dB), medium noise environment (30-60dB), and high noise environment (over 60dB). When the system calls the matching model, it automatically retrieves the locally stored data and maps the current sound field data to the corresponding threshold intervals, so that subsequent template selection is more accurate.

[0029] S103: Substitute each required parameter into the matching model to generate a corresponding sub-code. The sub-codes are then arranged sequentially according to the matching model's permutation rules to generate a corresponding matching code. In this embodiment, a sub-code is a coding method used to identify the characteristics of the sound field environment. It is an independent identifier formed by combining the classification results of each required parameter. Based on the preset matching model, the system converts the classification results of parameters such as room volume, reverberation time, and background noise into corresponding sub-codes. For example, assuming the room volume classification numbers are "1-Small," "2-Medium," and "3-Large," the reverberation time classification numbers are "1-Short Reverberation," "2-Medium Reverberation," and "3-Long Reverberation," and the background noise classification numbers are "1-Low Noise," "2-Medium Noise," and "3-High Noise," then a medium-sized room with a moderate reverberation time and low background noise may have a matching sub-code of "2-2-1." The permutation rules are used to ensure that the order of the different parameters is consistent, allowing for efficient subsequent template matching. For example, the matching code may follow the order of "room volume - reverberation time - background noise", so the final matching code may be "221". This method ensures that the same or similar sound field environment can be matched to a consistent template on different devices, thereby improving the standardization of tuning.

[0030] S104. Match the corresponding basic cloud template according to the matching code; in this embodiment, the matching code serves as a unique identifier of the sound field environment and is used to retrieve the basic template that best suits the current environment in the cloud database. The system will send the generated matching code to the local cache or cloud server and search the database for the tuning template corresponding to the matching code. For example, for the matching code "221", the system will prioritize the tuning template corresponding to medium room volume, medium reverberation time and low background noise, and download the template for subsequent audio signal processing. If there are multiple templates that meet the matching code in the cloud database, the system may further optimize the selection based on historical usage records or manually fine-tuned parameters, such as giving priority to templates that the user has used and adjusted in the past to ensure personalized and consistent sound quality. If the matching code fails to find a completely matching template in the cloud database, the system can also find the closest template through similarity matching, and optimize it in combination with local debugging in subsequent steps to ensure that the final tuning solution meets the requirements of the current sound field environment.

[0031] In one embodiment, if Figure 3 As shown, in step S30, that is, based on the preset equalization standard, performing equalization analysis on the spectrum characteristics and determining the corresponding equalization parameter set, the following steps are included: S301. Based on a preset equalization standard, the spectral characteristics are divided into multiple preset frequency bands, and the amplitude and phase characteristics of each preset frequency band are calculated. In this embodiment, the preset equalization standard refers to a set of audio frequency adjustment rules set for different application scenarios to ensure that the energy distribution of the audio signal in each frequency band meets the target sound quality requirements. Spectral characteristic division refers to decomposing the entire frequency range of the input audio signal into multiple distinct frequency bands. Each frequency band corresponds to a specific sound characteristic. For example, low frequencies (20Hz-250Hz) typically include drums and bass, mid-frequencies (250Hz-4kHz) primarily involve melodic instruments such as vocals and guitar, and high frequencies (4kHz-20kHz) affect the brightness and airiness of the sound. Calculating the amplitude value refers to measuring the signal energy in each frequency band to determine the volume level of that frequency band. For example, in a concert, excessive low-frequency amplitude may result in muddiness, while insufficient high-frequency amplitude may make the sound appear dull and weak. Phase characteristics refer to the relative temporal variations of an audio signal. They determine the interaction between different frequency components. Significant shifts in the phase characteristics can cause sound distortion or weaken the energy of specific frequency bands. Therefore, by dividing the audio signal's spectral characteristics into multiple preset frequency bands and calculating the amplitude and phase characteristics of each band, the system can more precisely analyze the audio signal's energy distribution, providing accurate data support for subsequent equalization adjustments.

[0032] S302. Calculate the corresponding adjustment amount based on the amplitude value. In this embodiment, the amplitude value refers to the energy level of the audio signal in different frequency bands, which directly affects the overall balance and clarity of the sound. Calculating the adjustment amount means calculating the gain or attenuation based on the deviation between the amplitude value of the current audio signal and the target equalization standard. For example, at a concert, if the system detects that the amplitude value of the low frequency band (20Hz-250Hz) is much higher than the preset standard, it means that the bass is too strong and may obscure the details of the human voice or other instruments. In this case, the calculated adjustment amount may result in appropriate attenuation of the low frequency band. Conversely, if the amplitude of the mid-high frequency (2kHz-6kHz) is detected to be low, the calculated adjustment amount may result in increasing the gain of this frequency band to enhance the clarity of the human voice. The calculation of the adjustment amount is usually performed using a logarithmic scale (dB) to ensure the accuracy of the adjustment amount and a natural auditory transition. For example, if the target equalization standard requires a -3dB amplitude value for a certain frequency band, and the currently detected amplitude value is +5dB, the adjustment amount is calculated to reduce the gain by 8dB to return the frequency band to a reasonable energy level. By accurately calculating the adjustment amount for each frequency band, the system can effectively balance the various frequency components of the audio signal, making the sound quality more balanced and natural.

[0033] S303. Calculate corresponding phase compensation parameters based on the phase characteristics. In this embodiment, phase characteristics refer to the relative time delay or lead of different frequency components, which significantly impacts sound clarity, spatiality, and layering. Calculating phase compensation parameters involves calculating the corresponding corrections for phase shifts that may be introduced during equalization to ensure that the audio signal maintains the correct phase relationship after adjustment. For example, in traditional equalization, large gains or reductions can introduce phase distortion, causing certain frequency components to delay or cancel each other out, resulting in an unnatural sound. To address this issue, the system calculates appropriate compensation parameters based on the phase characteristics of the input signal using a minimum phase filter or linear phase equalizer to correct for phase errors caused by equalization. For example, if the phase of a frequency band shifts by 5 degrees during audio processing, the system may calculate a phase compensation parameter to restore the equalized signal to its original phase, thereby maintaining sound clarity and layering. By calculating and applying the phase compensation parameters, phase distortion in the audio signal can be effectively reduced, making the equalized sound more natural and realistic.

[0034] S304: Integrate the adjustment values ​​and phase compensation parameters to determine a corresponding equalization parameter set. In this embodiment, the equalization parameter set refers to the final set of parameters used to guide equalization adjustment, including gain adjustment values ​​and phase compensation parameters for each frequency band. Integrating the adjustment values ​​and phase compensation parameters involves combining the previously calculated gain adjustment data with the phase correction data to ensure that the equalization adjustment not only optimizes the energy distribution of each frequency band but also avoids phase distortion. For example, in a professional recording studio, the system may require a -3dB attenuation of the low frequencies, while the calculated phase compensation parameters may need to be corrected forward by 2 degrees to ensure that the adjusted audio signal neither over-boosts nor weakens certain frequencies nor disrupts the temporal relationship of the original signal. The final determination of the equalization parameter set is typically based on an automated optimization algorithm, such as an adaptive filter or artificial intelligence model, to select the optimal solution from multiple adjustment options to ensure that the equalization adjustment meets the target sound quality standards without adversely affecting the overall listening experience of the audio signal. The resulting equalization parameter set can be used for real-time tuning or stored as a preset template for reuse in similar sound field environments, thereby improving the system's intelligence and tuning efficiency.

[0035] Specifically, after the system obtains the gain adjustment and phase compensation parameters corresponding to each frequency band in S302 and S303 respectively, when entering the S304 stage, the system comprehensively processes these two types of parameters based on a set of objective functions for equalization optimization. The objective function not only includes the requirements for the balance of frequency energy distribution, but also combines the constraints of signal fidelity, auditory stability and phase continuity. To this end, the system calls the embedded automatic optimization algorithm to search and evaluate all parameter combinations. In the current embodiment, the automatic optimization algorithm is an adaptive multi-objective optimization algorithm that integrates a hybrid modeling method of gradient descent and genetic strategy, and can efficiently converge to the optimal or approximately optimal equalization parameter set solution in the high-dimensional parameter space. During the optimization process, the system will first construct an initial parameter solution set based on the spectral characteristics of the input audio signal, and then score the optimization effect of each set of parameter combinations by setting a loss function. The loss function comprehensively considers factors such as the smoothness of energy transition between frequency bands, the degree of compliance with the target sound quality standard, and the group delay change caused by the phase difference. On this basis, the automatic optimization algorithm dynamically updates the parameter solution space based on each scoring result, continuously iterating and selecting the parameter set that best meets the target sound performance under the current sound field conditions. This process uses a dynamic step-size adjustment mechanism to avoid falling into local optimality, and introduces preset rules based on the statistical results of historical sound field data to generate the initial solution, improving search efficiency and convergence speed.

[0036] In one embodiment, if Figure 4As shown, in step S40, that is, based on the gain control strategy, the corresponding gain compensation amount is calculated according to the dynamic range characteristics, and the step of determining the corresponding gain parameter set according to the gain compensation amount includes: S401. Based on the gain control strategy, determine the corresponding extraction rule, target amplitude value, and gain adjustment threshold. In this embodiment, the gain control strategy is a method that adaptively adjusts gain based on the dynamic range characteristics of the input audio signal to ensure balanced volume across different channels while preventing overload distortion or signal clipping. The extraction rule refers to a standardized method for analyzing and calculating the amplitude characteristics of an audio signal. Different calculation methods can be set based on factors such as the signal's temporal distribution and spectral energy distribution. For example, for vocal signals, the system may use peak detection, while for background music signals, the system may use a root mean square (RMS) calculation method to obtain more stable gain adjustment data. The target amplitude value is a reference standard value set by the system, typically based on empirical data or audio processing specifications. For example, in a broadcast system, the target amplitude value for vocals may be set to -20 dBFS to ensure consistent voice levels across different speakers. The gain adjustment threshold is used to limit the range of gain adjustment to prevent the system from over-amplifying or over-attenuating the signal. For example, in an audio system, to prevent noise from being amplified, the gain adjustment threshold may be set to ±6dB. That is, if the calculated gain adjustment exceeds 6dB, the system will automatically limit its gain change to ensure stable audio output.

[0037] S402. Based on the extraction rules, extract the average amplitude value from the dynamic range feature. The dynamic range feature is a statistical result of the maximum amplitude, minimum amplitude, average amplitude, and signal crest factor of the input audio signal. In this embodiment, the dynamic range feature refers to the amplitude variation of the audio signal in the time dimension, which can reflect the overall loudness and transient characteristics of the signal. The maximum amplitude value represents the peak intensity of the signal within a certain time period, the minimum amplitude value represents the lowest intensity of the signal, and the average amplitude value represents the average energy level of the signal throughout the entire time period. The crest factor is the ratio of the maximum amplitude value to the root mean square (RMS) value and is generally used to measure the dynamic characteristics of a signal. For example, a drum signal containing a lot of percussive sound has a high crest factor, while a compressed music signal has a low crest factor. The process of extracting the average amplitude value is based on the previously set extraction rules. The system calculates the short-term energy of the input audio signal and takes the average value over a period of time to obtain a more stable gain adjustment parameter. For example, in a conference room environment, if the system detects that the average amplitude value of the speaker is significantly lower than the target amplitude value, it means that the volume of the channel is low and the gain needs to be increased. If the amplitude value is detected to be too high, the gain may need to be attenuated to prevent distortion caused by excessive sound.

[0038] S403. Calculate the gain compensation between the average amplitude value and the target amplitude value. In this embodiment, the gain compensation refers to the difference between the average amplitude value of the input audio signal and the target amplitude value. It is used to determine whether the signal needs to be boosted or attenuated. The method for calculating the gain compensation is typically based on logarithmic calculations to ensure natural audio adjustments and smooth auditory perception. For example, if the target amplitude value is set to -18dBFS and the actual detected average amplitude value is -24dBFS, the calculated gain compensation is +6dB, meaning the signal needs to be boosted by 6dB to match the target loudness standard. Conversely, if the detected average amplitude value is -12dBFS, the calculated gain compensation is -6dB, requiring a gain reduction to prevent signal overload. The gain compensation calculation typically uses a sliding window technique to avoid gain fluctuations caused by sudden signals. For example, during live tuning at a concert, some instruments may experience instantaneous volume changes. The system can use a sliding window to smooth the gain compensation to ensure that volume adjustments during tuning do not produce abrupt changes.

[0039] S404. Based on the gain adjustment threshold, the gain compensation amount is amplitude constrained to generate a corresponding gain parameter set. In this embodiment, the gain adjustment threshold is used to limit the adjustment amplitude of the gain compensation amount to prevent excessive gain changes from causing sound quality distortion or audio signal imbalance. Amplitude constraint processing refers to limiting or adjusting the gain compensation amount so that it conforms to the set gain adjustment range. For example, if the calculated gain compensation amount is +8dB, but the system-set upper limit of the gain adjustment threshold is +6dB, the system will constrain the gain adjustment amount to +6dB to prevent excessive volume increase and clipping distortion. Similarly, if the calculated gain compensation amount is -10dB, and the system lower limit is set to -6dB, the system will adjust it to -6dB to prevent excessive volume attenuation from affecting auditory perception. The process of generating a gain parameter set is to store the final calculated gain compensation amount in a parameter list and apply it to the gain adjustment of each channel. For example, in a broadcast system, the system generates a gain parameter set for each channel, which contains the gain adjustment value calculated in real time, and dynamically adjusts the gain of each channel during audio signal processing to ensure that the volume of all audio sources remains consistent, improving the overall balance and listening experience of the audio.

[0040] In one embodiment, if Figure 5 As shown, in step S50, that is, the step of determining the corresponding filter parameter set according to the howling frequency component, the following steps are included: S501. Perform spectrum analysis on the howling frequency component to determine its corresponding center frequency and bandwidth. In this embodiment, the howling frequency component refers to specific frequency noise generated by the closed feedback loop formed after sound is transmitted from the speaker to the microphone. It typically manifests as a high-amplitude, stable, narrowband peak signal. Spectral analysis involves performing a fast Fourier transform (FFT) or short-time Fourier transform (STFT) on the input audio signal to obtain the signal's energy distribution in the frequency domain and detect whether there are abnormally high-energy peaks. The center frequency is the frequency point at which the howling signal is strongest, typically in the range of 2kHz to 6kHz. The bandwidth refers to the spectral range of the howling signal, generally determined by the portion of the frequency peak extending to either side. For example, in a conference room, if the speaker is close to the microphone, the system may detect an abnormally strong narrowband signal at 4.2kHz and calculate the signal's bandwidth to be 200Hz, meaning that the howling signal is primarily concentrated between 4.1kHz and 4.3kHz. By accurately determining the center frequency and bandwidth of the howling signal, the system can perform subsequent filtering processing more specifically to effectively suppress the howling while minimizing the impact on other audio components.

[0041] S502. Determine the initial filtering parameters for the notch filter based on the center frequency and bandwidth. In this embodiment, the notch filter is a filter specifically designed to attenuate specific frequency components. Its design goal is to accurately reduce energy at the howling frequency without affecting audio signals in other frequency ranges. The initial filtering parameters include the center frequency, bandwidth, and filter depth of the notch filter. These parameters are determined based on the howling signal characteristics obtained in the previous step. The center frequency matches the previously calculated howling center frequency, while the bandwidth is set based on the extended range of the howling signal. For example, if the detected howling signal has a center frequency of 4.2kHz and a bandwidth of 200Hz, the system may set the center frequency of the notch filter to 4.2kHz and the bandwidth to an appropriate range, such as 180Hz to 220Hz, to ensure accurate energy reduction. The filter depth determines the degree of attenuation at that frequency point and is typically in the range of -3dB to -30dB, dynamically adjusted based on the intensity of the howling signal. For example, if the detected howling signal is strong, a deeper attenuation of -24dB may be required, while for a milder howling signal, a -6dB attenuation may be used to avoid a significant impact on the sound quality. By properly setting the initial parameters of the notch filter, the system can suppress howling while preserving the naturalness of the audio signal as much as possible, ensuring that the clarity of speech or music is not affected.

[0042] S503. Dynamically adaptively adjust the initial filter parameters to generate a corresponding filter parameter set. In this embodiment, dynamic adaptive adjustment refers to dynamically optimizing the notch filter parameters based on real-time audio signal changes to ensure optimal filtering while avoiding excessive impact on normal audio signals. This adjustment process is typically based on a feedback control mechanism. After applying the initial filter parameters, the system continues to monitor the spectral distribution of the input signal and assesses whether residual howling is still present or whether it has caused additional attenuation of the normal audio signal. If the system detects that the notch filter fails to fully suppress the howling signal, it further increases the attenuation depth or fine-tunes the bandwidth to enhance the howling suppression capability. For example, in a noisy concert hall environment, if the initial notch depth is set to -12dB but residual howling signals are still detected, the system may adjust it to -18dB to completely suppress the howling frequency. Furthermore, to prevent the notch filter from affecting the sound quality of adjacent frequencies, the system may monitor the energy changes at the frequency point over multiple consecutive time windows and adjust the bandwidth based on dynamic changes. For example, in a speech scenario, if the speaker moves, causing the howling frequency to drift, the system automatically tracks this drift and adjusts the center frequency of the notch filter accordingly, ensuring that it always accurately covers the howling signal without incorrectly attenuating other frequency components. The resulting filter parameter set includes the dynamically adjusted center frequency, bandwidth, and filter depth, and can be stored in a local cache or cloud database for rapid loading and application in similar environments in the future, thereby improving the system's adaptability and the degree of automatic tuning.

[0043] In one embodiment, if Figure 6 As shown, in step S60, i.e., the step of establishing a corresponding local cache template according to the reverberation parameter, the equalization parameter set, the gain parameter set, and the filter parameter set, the steps include: S601. Normalize the reverberation parameters, equalization parameter sets, gain parameter sets, and filter parameter sets to generate corresponding template parameter sets. In this embodiment, normalization refers to converting different types of audio processing parameters into a unified data format and range to ensure consistency during subsequent calculation and storage, and to avoid calculation errors caused by data differences at different scales. Reverberation parameters describe the reflection characteristics of audio signals in different spatial environments, typically including reverberation time (RT60), early reflection intensity, and reverberation diffusion coefficient. They determine the delay and spatial perception of sound within a room. The equalization parameter set is gain adjustment data for different frequency bands, including gain values ​​and phase compensation information for each frequency band, used to balance the spectral distribution of the audio signal. The gain parameter set controls the volume variation of the input signal, ensuring consistent volume levels across different sound sources and preventing sudden fluctuations in sound volume. The filter parameter set is a set of parameters used for howling suppression or noise filtering, primarily including the center frequency, bandwidth, and attenuation depth of the notch filter. Specific methods for normalization can include standardization, min-max scaling, and logarithmic transformation. For example, if the reverberation time data ranges from 0.1 to 3 seconds, and the equalization gain data ranges from -12dB to +12dB, normalization can convert them to the same numerical range, such as 0 to 1, so that all parameters can be optimized within the same calculation framework. Through normalization, different types of audio parameters can be more efficiently stored and matched in the subsequent process, improving the accuracy of data calculation and system adaptability.

[0044] S602: Generate a multidimensional parameter mapping table based on the template parameter set. In this embodiment, a multidimensional parameter mapping table refers to a data structure used to store and manage the relationships between various normalized audio parameters. This mapping table arranges reverberation parameters, equalization parameters, gain parameters, and filter parameters according to different dimensions, enabling the system to quickly retrieve and match the appropriate parameter configuration when applying a locally cached template. The mapping table construction method is typically based on a multidimensional matrix or database indexing mechanism. For example, the system can divide different audio parameters into multiple levels, such as sound field type (small room, large conference hall, concert hall, etc.), signal type (speech, music, ambient sound, etc.), and target sound quality requirement (clarity priority, naturalness priority, etc.). Appropriate mapping rules can then be set for each parameter at each level. For example, if the current sound field type is a medium-sized conference room, the signal type is speech, and the target sound quality requirement is clarity priority, the system can find the optimal equalization, gain, and filter parameters for this scenario in the mapping table and apply the corresponding reverberation adjustments. The advantage of the mapping table is that it can achieve fast matching through hierarchical indexing, avoiding the computational delay caused by traditional linear search methods, thereby improving the real-time and adaptability of the audio debugging system.

[0045] S603. Perform hierarchical optimization on the multidimensional parameter mapping table to generate a corresponding local cache template. In this embodiment, hierarchical optimization refers to structurally optimizing the data in the multidimensional parameter mapping table to improve data matching accuracy and retrieval efficiency while reducing storage redundancy. Optimization typically includes parameter clustering, redundant parameter removal, and dynamic weight adjustment. For example, based on a large amount of historical audio tuning data, the system can analyze which parameter combinations are most frequently used and prioritize them, while reducing or removing less commonly used or potentially interfering parameter combinations to improve matching efficiency. Furthermore, hierarchical optimization can adjust parameter weights for different application scenarios. For example, in a music performance, equalization parameters may have a higher priority, while in a conference room, gain control may be more critical. By performing hierarchical optimization on the mapping table, the system can ensure that the local cache template more accurately matches the current audio environment, while reducing unnecessary data storage and improving overall system efficiency. Once the final optimized parameter set is stored in the local cache, the system can directly call the template in subsequent similar environments, avoiding the need to recalculate from scratch each time tuning, thereby improving the efficiency of automatic tuning and the user experience.

[0046] It should be understood that the size of the serial numbers of the steps in the above embodiments 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 this application.

[0047] In one embodiment, a multimedia mixing console automatic debugging device is provided, and the multimedia mixing console automatic debugging device corresponds to the multimedia mixing console automatic debugging method in the above embodiment. Figure 7 As shown, the automatic debugging device for a multimedia mixing console includes a first acquisition module, a second acquisition module, a first determination module, a second determination module, a third determination module, and a judgment module. The functional modules are described in detail as follows: A first acquisition module is configured to acquire sound field spatial data and match a pre-cached basic cloud template according to the sound field spatial data, wherein the basic cloud template includes at least a preset equalization standard, a gain control strategy, and a reverberation parameter; A second acquisition module is used to acquire a multi-channel input audio signal and determine a frequency spectrum feature and a dynamic range feature of the input audio signal; A first determining module, configured to perform an equalization analysis on the spectrum characteristics based on the preset equalization standard to determine a corresponding equalization parameter set; A second determining module is configured to calculate a corresponding gain compensation amount according to the dynamic range characteristics based on the gain control strategy, and determine a corresponding gain parameter set according to the gain compensation amount; a third determination module, configured to perform a feedback noise detection operation on the input audio signal, and if a howling frequency component is determined to exist, determine a corresponding filter parameter set according to the howling frequency component; A judgment module is used to determine whether the basic cloud template needs to be updated. If so, a corresponding local cache template is established according to the reverberation parameters, the equalization parameter set, the gain parameter set and the filter parameter set; if not, the basic cloud template is determined to be a local cache template.

[0048] Optionally, the first acquisition module includes: a first extraction unit, configured to extract required parameters of the sound field spatial data, wherein the required parameters include at least room volume, reverberation time, and background noise; A calling unit, configured to call a matching model cached locally, wherein the matching model is provided with a plurality of threshold intervals for each of the demand parameters; a first generating unit, configured to substitute each of the requirement parameters into the matching model to generate a corresponding sub-code, and sequentially arrange each of the sub-codes according to an arrangement rule of the matching model to generate a corresponding matching code; A matching unit, configured to match a corresponding basic cloud template according to the matching code; Optionally, the first determining module includes: a dividing unit, configured to divide the spectrum characteristics into a plurality of preset frequency bands based on the preset equalization standard, and calculate the amplitude value and phase characteristics of each of the preset frequency bands; A first calculation unit, configured to calculate a corresponding adjustment amount according to the amplitude value; A second calculation unit is used to calculate a corresponding phase compensation parameter according to the phase feature; an integration unit, configured to integrate the adjustment amount and the phase compensation parameter to determine a corresponding equalization parameter set; Optionally, the second determining module includes: A first determining unit is configured to determine a corresponding extraction rule, a target amplitude value, and a gain adjustment threshold according to the gain control strategy; a second extraction unit, configured to extract an average amplitude value from the dynamic range feature based on the extraction rule, wherein the dynamic range feature is a statistical result of a maximum amplitude value, a minimum amplitude value, an average amplitude value, and a signal crest factor of the input audio signal; a third calculation unit, configured to calculate a gain compensation amount of the average amplitude value and the target amplitude value; a second generating unit, configured to perform amplitude constraint processing on the gain compensation amount according to the gain adjustment threshold, so as to generate a corresponding gain parameter set; Optionally, the third determining module includes: a second determining unit, configured to perform a spectrum analysis operation on the howling frequency component to determine a corresponding center frequency and bandwidth; a third determining unit, configured to determine initial filtering parameters applied to a notch filter according to the center frequency and the bandwidth; an adjusting unit, configured to perform dynamic adaptive adjustment on the initial filtering parameters to generate a corresponding filtering parameter set; Optionally, the judgment module includes: a third generating unit, configured to perform normalization processing on the reverberation parameter, the equalization parameter set, the gain parameter set, and the filter parameter set to generate a corresponding template parameter set; a fourth generating unit, configured to generate a multidimensional parameter mapping table according to the template parameter set; The fifth generating unit is configured to perform hierarchical optimization processing on the multi-dimensional parameter mapping table to generate a corresponding local cache template.

[0049] The specific definition of a multimedia mixing console automatic debugging device can be found in the definition of a multimedia mixing console automatic debugging method described above and will not be repeated here. Each module in the aforementioned multimedia mixing console automatic debugging device can be implemented in whole or in part through software, hardware, or a combination thereof. Each of the aforementioned modules can be embedded in or independent of a processor in a computer device in hardware form, or can be stored in a computer device memory in software form, so that the processor can call and execute the corresponding operations of each of the aforementioned modules.

[0050] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 8 As shown. The computer device includes a processor, memory, network interface, and database connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a method for automatically debugging a multimedia mixing console is implemented.

[0051] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the following steps are performed: S10, obtaining sound field spatial data, and matching a pre-cached basic cloud template according to the sound field spatial data, where the basic cloud template includes at least a preset equalization standard, a gain control strategy, and a reverberation parameter; S20, obtaining a multi-channel input audio signal, and determining a frequency spectrum characteristic and a dynamic range characteristic of the input audio signal; S30, performing an equalization analysis on the spectrum characteristics based on a preset equalization standard, and determining a corresponding equalization parameter set; S40, based on the gain control strategy, calculating the corresponding gain compensation amount according to the dynamic range characteristics, and determining the corresponding gain parameter set according to the gain compensation amount; S50, performing a feedback noise detection operation on the input audio signal, and if it is determined that a howling frequency component exists, determining a corresponding filter parameter set according to the howling frequency component; S60: Determine whether the basic cloud template needs to be updated. If so, establish a corresponding local cache template based on the reverberation parameter, equalization parameter set, gain parameter set, and filter parameter set. If not, determine that the basic cloud template is the local cache template. In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented: S10, obtaining sound field spatial data, and matching a pre-cached basic cloud template according to the sound field spatial data, where the basic cloud template includes at least a preset equalization standard, a gain control strategy, and a reverberation parameter; S20, obtaining a multi-channel input audio signal, and determining a frequency spectrum characteristic and a dynamic range characteristic of the input audio signal; S30, performing an equalization analysis on the spectrum characteristics based on a preset equalization standard, and determining a corresponding equalization parameter set; S40, based on the gain control strategy, calculating the corresponding gain compensation amount according to the dynamic range characteristics, and determining the corresponding gain parameter set according to the gain compensation amount; S50, performing a feedback noise detection operation on the input audio signal, and if it is determined that a howling frequency component exists, determining a corresponding filter parameter set according to the howling frequency component; S60: Determine whether the basic cloud template needs to be updated. If so, establish a corresponding local cache template based on the reverberation parameter, equalization parameter set, gain parameter set, and filter parameter set. If not, determine that the basic cloud template is the local cache template. Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0052] Those skilled in the art will clearly understand that for the sake of convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0053] The above-described embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present application, and should all be included in the scope of protection of the present application.

Claims

1. A multimedia mixing console automatic debugging method, characterized in that: The automatic debugging method of a multimedia mixing console comprises: Acquiring sound field spatial data, and matching a pre-cached basic cloud template according to the sound field spatial data, wherein the basic cloud template includes at least a preset equalization standard, a gain control strategy, and a reverberation parameter; Acquire a multi-channel input audio signal, and determine a frequency spectrum characteristic and a dynamic range characteristic of the input audio signal; Based on the preset equalization standard, performing equalization analysis on the spectrum characteristics to determine a corresponding equalization parameter set; Based on the gain control strategy, calculating a corresponding gain compensation amount according to the dynamic range characteristics, and determining a corresponding gain parameter set according to the gain compensation amount; Performing a feedback noise detection operation on the input audio signal, and if it is determined that a howling frequency component exists, determining a corresponding filter parameter set according to the howling frequency component; Determine whether the basic cloud template needs to be updated. If so, establish a corresponding local cache template based on the reverberation parameters, the equalization parameter set, the gain parameter set, and the filter parameter set. If not, determine that the basic cloud template is a local cache template.

2. The automatic debugging method of a multimedia mixing console according to claim 1, characterized in that: The step of matching a pre-cached basic cloud template according to the sound field spatial data includes: Extracting required parameters of the sound field space data, wherein the required parameters include at least room volume, reverberation time, and background noise; Invoking a matching model cached locally, wherein the matching model is provided with a plurality of threshold intervals for each of the demand parameters; Substituting each of the requirement parameters into the matching model to generate a corresponding sub-code, and sequentially arranging each of the sub-codes according to an arrangement rule of the matching model to generate a corresponding matching code; The corresponding basic cloud template is matched according to the matching code.

3. The automatic debugging method of a multimedia mixing console according to claim 1, characterized in that: The step of performing equalization analysis on the spectrum characteristics based on the preset equalization standard to determine a corresponding equalization parameter set includes: Based on the preset equalization standard, the spectrum characteristics are divided into a plurality of preset frequency bands, and the amplitude value and phase characteristics of each of the preset frequency bands are calculated; Calculating a corresponding adjustment amount according to the amplitude value; Calculating corresponding phase compensation parameters according to the phase characteristics; The adjustment amount and the phase compensation parameter are integrated to determine a corresponding equalization parameter set.

4. The automatic debugging method of a multimedia mixing console according to claim 1, characterized in that: The step of calculating a corresponding gain compensation amount according to the dynamic range characteristics based on the gain control strategy, and determining a corresponding gain parameter set according to the gain compensation amount includes: According to the gain control strategy, determining corresponding extraction rules, target amplitude value and gain adjustment threshold; Extracting an average amplitude value from the dynamic range feature based on the extraction rule, wherein the dynamic range feature is a statistical result of a maximum amplitude value, a minimum amplitude value, an average amplitude value, and a signal crest factor of the input audio signal; Calculating a gain compensation amount of the average amplitude value and the target amplitude value; According to the gain adjustment threshold, amplitude constraint processing is performed on the gain compensation amount to generate a corresponding gain parameter set.

5. The automatic debugging method of a multimedia mixing console according to claim 1, characterized in that: The step of determining a corresponding filter parameter set according to the howling frequency component includes: Performing a spectrum analysis operation on the howling frequency component to determine the corresponding center frequency and bandwidth; Determining initial filtering parameters for a notch filter based on the center frequency and bandwidth; Dynamically adaptively adjusting the initial filtering parameters to generate a corresponding filtering parameter set.

6. The automatic debugging method of a multimedia mixing console according to claim 1, characterized in that: The step of establishing a corresponding local cache template according to the reverberation parameter, the equalization parameter set, the gain parameter set, and the filter parameter set includes: performing normalization processing on the reverberation parameter, the equalization parameter set, the gain parameter set, and the filter parameter set to generate a corresponding template parameter set; Generate a multidimensional parameter mapping table according to the template parameter set; A hierarchical optimization process is performed on the multidimensional parameter mapping table to generate a corresponding local cache template.

7. A multimedia mixing console automatic debugging device, characterized in that: The automatic debugging device for a multimedia mixing console comprises: A first acquisition module is configured to acquire sound field spatial data and match a pre-cached basic cloud template according to the sound field spatial data, wherein the basic cloud template includes at least a preset equalization standard, a gain control strategy, and a reverberation parameter; A second acquisition module is used to acquire a multi-channel input audio signal and determine a frequency spectrum feature and a dynamic range feature of the input audio signal; A first determining module, configured to perform an equalization analysis on the spectrum characteristics based on the preset equalization standard to determine a corresponding equalization parameter set; A second determining module is configured to calculate a corresponding gain compensation amount according to the dynamic range characteristics based on the gain control strategy, and determine a corresponding gain parameter set according to the gain compensation amount; a third determination module, configured to perform a feedback noise detection operation on the input audio signal, and if a howling frequency component is determined to exist, determine a corresponding filter parameter set according to the howling frequency component; A judgment module is used to determine whether the basic cloud template needs to be updated. If so, a corresponding local cache template is established according to the reverberation parameters, the equalization parameter set, the gain parameter set and the filter parameter set; if not, the basic cloud template is determined to be a local cache template.

8. The automatic debugging device for a multimedia mixing console according to claim 7, characterized in that: The first acquisition module includes: a first extraction unit, configured to extract required parameters of the sound field spatial data, wherein the required parameters include at least room volume, reverberation time, and background noise; A calling unit, configured to call a matching model cached locally, wherein the matching model is provided with a plurality of threshold intervals for each of the demand parameters; a first generating unit, configured to substitute each of the requirement parameters into the matching model to generate a corresponding sub-code, and sequentially arrange each of the sub-codes according to an arrangement rule of the matching model to generate a corresponding matching code; A matching unit, configured to match a corresponding basic cloud template according to the matching code; The first determining module includes: a dividing unit, configured to divide the spectrum characteristics into a plurality of preset frequency bands based on the preset equalization standard, and calculate the amplitude value and phase characteristics of each of the preset frequency bands; A first calculation unit, configured to calculate a corresponding adjustment amount according to the amplitude value; A second calculation unit is used to calculate a corresponding phase compensation parameter according to the phase feature; an integration unit, configured to integrate the adjustment amount and the phase compensation parameter to determine a corresponding equalization parameter set; The second determining module includes: A first determining unit is configured to determine a corresponding extraction rule, a target amplitude value, and a gain adjustment threshold according to the gain control strategy; a second extraction unit, configured to extract an average amplitude value from the dynamic range feature based on the extraction rule, wherein the dynamic range feature is a statistical result of a maximum amplitude value, a minimum amplitude value, an average amplitude value, and a signal crest factor of the input audio signal; a third calculation unit, configured to calculate a gain compensation amount of the average amplitude value and the target amplitude value; a second generating unit, configured to perform amplitude constraint processing on the gain compensation amount according to the gain adjustment threshold, so as to generate a corresponding gain parameter set; The third determining module includes: a second determining unit, configured to perform a spectrum analysis operation on the howling frequency component to determine a corresponding center frequency and bandwidth; a third determining unit, configured to determine initial filtering parameters applied to a notch filter according to the center frequency and the bandwidth; an adjusting unit, configured to perform dynamic adaptive adjustment on the initial filtering parameters to generate a corresponding filtering parameter set; The judgment module includes: a third generating unit, configured to perform normalization processing on the reverberation parameter, the equalization parameter set, the gain parameter set, and the filter parameter set to generate a corresponding template parameter set; a fourth generating unit, configured to generate a multidimensional parameter mapping table according to the template parameter set; The fifth generating unit is configured to perform hierarchical optimization processing on the multi-dimensional parameter mapping table to generate a corresponding local cache template.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the automatic debugging method for a multimedia mixing console as described in any one of claims 1 to 6 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the automatic debugging method for a multimedia mixing console as claimed in any one of claims 1 to 6 are implemented.

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