A method, device and equipment for adaptively adjusting the microphone balance

Abstract

The present invention discloses a method, device and equipment for adaptively adjusting the microphone balance. The method includes: collecting and analyzing the audio signal of the singer; performing time-frequency conversion on the collected audio signal to generate a real-time spectrogram; comparing the real-time spectrum with the reference spectrum to calculate the difference between each frequency band and the reference spectrum, where the reference spectrum is an ideal sound pre-recorded or a target spectrum set in the system; and adjusting the gain setting of the equalizer in real time according to the difference of each frequency band, so that the microphone can automatically adjust the frequency response according to the voice characteristics of the singer in real time. The technical solution of the present invention aims to improve the accuracy and flexibility of microphone balance adjustment and enhance the recording and performance effects.

Description

Technical Field

[0001] The present invention relates to the technical field of microphones, and particularly to a method, device, and equipment for adaptively adjusting the microphone balance. Background Art

[0002] In modern music recording and live performances, the selection and setting of microphones are crucial for the presentation of sound quality. Since the vocal ranges, vocalization methods, and music styles of singers vary, the equalization adjustment of microphones must be able to adapt to these differences to ensure the best sound effects during recording and performances. Traditional microphone equalization settings usually rely on manual adjustment, requiring audio engineers to have a high level of professional skills. However, this method is not only time-consuming and laborious but also unable to adapt in real time to the vocal range changes and sound quality differences that may occur to singers during the singing process.

[0003] Currently, in order to improve the efficiency and accuracy of microphone equalization adjustment, some automatic equalization technologies have emerged. These technologies can automatically adjust the equalization settings of microphones based on the voice characteristics of singers. However, existing automatic equalization technologies usually rely on preset parameters and cannot fully consider the individual voice characteristics of singers and the overtone structure in specific music segments. This results in limitations in the precision and flexibility of microphone equalization adjustment. Especially when facing complex acoustic environments or professional recordings requiring high sound quality, existing technologies often fail to meet the requirements. Summary of the Invention

[0004] The main objective of the present invention is to provide a method, device, and equipment for adaptively adjusting the microphone balance, aiming to improve the precision and flexibility of microphone equalization adjustment and enhance the recording and performance effects.

[0005] To achieve the above objective, the embodiments of the present application adopt the following technical solutions.

[0006] In a first aspect, the present invention provides a method for adaptively adjusting the microphone balance, including:

[0007] Collect and analyze the audio signal of the singer;

[0008] Perform time-frequency conversion on the collected audio signal to generate a real-time spectrogram;

[0009] Compare the real-time spectrum with a reference spectrum, and calculate the difference between each frequency band and the reference spectrum, where the reference spectrum is an ideal sound pre-recorded or a target spectrum set in the system;

[0010] According to the difference of each frequency band, adjust the gain setting of the equalizer in real time, so that the microphone can automatically adjust the frequency response according to the voice characteristics of the singer in real time.

[0011] In a possible implementation, after the step of adjusting the gain setting of the equalizer in real time according to the difference of each frequency band, the following steps are further included:

[0012] Continuously monitor the adjusted audio output to check the effect of equalization adjustment;

[0013] Collect the feedback of the singer and adjust the gain setting of the equalizer.

[0014] In a possible implementation, in the step of continuously monitoring the adjusted audio output to check the effect of equalization adjustment, the following steps are further included:

[0015] Filter the ambient noise to ensure that the equalization adjustment focuses on the useful signal.

[0016] In a possible implementation, in the step of collecting and analyzing the audio signal of the singer, it includes:

[0017] Collect and analyze the vocal range width of the singer, and mark the lowest and highest frequency ranges within which the singer can accurately produce sounds;

[0018] Collect and analyze the overtone characteristics of the singer, where the overtone characteristics refer to other frequency components except the fundamental frequency and are used to determine the timbre and texture of the sound.

[0019] In a possible implementation, the step of performing time-frequency conversion on the collected audio signal to generate a real-time spectrogram includes:

[0020] Divide the audio signal into small window blocks, and the time of each window is 20 ms - 50 ms;

[0021] Apply the Fourier transform to each window to obtain spectral information.

[0022] In a possible implementation, in the step of adjusting the gain setting of the equalizer in real time according to the difference of each frequency band, the calculation method of the frequency band interpolation is D(f,t) = T(f) - |X(f,t)|, where D(f,t) represents the spectral difference at frequency f at the current moment t, and |X(f,t)| is the amplitude of the input signal spectrum.

[0023] In a possible implementation, the method for gain setting is G(f,t) = αD(f,t) + Gprev(f,t), where α is an adjustment coefficient that controls the rate of gain adjustment, and Gprev(f,t) is the gain at the previous moment, which is used for smooth transition.

[0024] In a second aspect, the present invention provides a microphone equalization adaptive adjustment device, including:

[0025] A collection module for collecting and analyzing the audio signal of the singer;

[0026] A conversion module for performing time-frequency conversion on the collected audio signal to generate a real-time spectrogram;

[0027] A calculation gain module for comparing the spectrum of the current audio with the reference spectrum and calculating the frequency bands that need to be adjusted;

[0028] An adjustment gain module for real-time adjustment of the gain settings of the equalizer.

[0029] In a third aspect, the present invention provides a device, including: a processor and a memory, the memory is coupled to the processor, the memory is used to store computer program code, the computer program code includes computer instructions, when the processor reads the computer instructions from the memory, so that the electronic device executes the microphone equalization adaptive adjustment method as described in the first aspect or any possible implementation manner of the first aspect above.

[0030] The technical solution of the present invention generates a real-time spectrogram by performing time-frequency conversion on the collected audio signal; compares the real-time spectrum with the reference spectrum, calculates the difference between each frequency band and the reference spectrum, and the reference spectrum is an ideal sound pre-recorded or a target spectrum set in the system; according to the difference of each frequency band, the gain settings of the equalizer are adjusted in real time, so that the microphone can automatically adjust the frequency response according to the voice characteristics of the singer in real time. By analyzing in detail the vocal range and overtone characteristics of the singer and combining with the reference audio, the equalization settings of the microphone are dynamically adjusted, thereby realizing adaptive sound quality optimization; through such fine adjustment, the microphone can better capture and present the voice characteristics of the singer. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on the structures shown in these drawings.

[0032] Figure 1 It is a flowchart of an embodiment of the microphone equalization adaptive adjustment method of the present invention.

[0033] The realization of the purpose of the present invention, the functional characteristics and advantages will be further described in conjunction with the embodiments with reference to the drawings. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0034] In order to make the objectives, technical solutions, and advantages of this application clearer, the following further details this application in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not used to limit this application.

[0035] In response to the problems in the background technology, the embodiments of this application adopt the following technical solutions.

[0036] In a first aspect, with reference to Figure 1 as shown, the present invention provides a method for adaptively adjusting the microphone balance, including the following steps:

[0037] S10. Collect and analyze the audio signal of the singer.

[0038] In this step, first, collect the singer singing "do, re, mi, fa, so, la, si, do..." in the specified scale order, covering all pitches within their vocal range. During the collection process, use high-quality microphones and recording equipment to record the singer's voice to ensure the clarity and accuracy of the recording. Perform time-frequency analysis on the recorded sound. Usually, use the Fourier transform (FFT) or short-time Fourier transform (STFT) to convert the audio signal into the frequency domain; analyze the frequency components of each scale, determine the upper and lower limits of the singer's vocal range, and calculate the width of the singer's vocal range. Based on the frequency analysis results, mark the lowest and highest frequency ranges within which the singer can accurately produce sounds, thereby determining the width of their vocal range. These data will provide a basis for the adaptive adjustment of the subsequent equalizer.

[0039] Secondly, collect a representative music segment sung by the singer and also record it using high-quality recording equipment. Perform time-frequency conversion analysis on the recorded music, pay attention to the main frequency and harmonic components in the spectrum, and record the harmonic distribution and intensity in each frequency band (bass, midrange, treble). Harmonics refer to other frequency components besides the fundamental frequency, and they determine the timbre and texture of the sound. By analyzing the harmonics, the unique characteristics of the singer's voice can be understood.

[0040] S20. Perform time-frequency conversion on the collected audio signal to generate a real-time spectrogram.

[0041] In this step, the time-frequency conversion transforms the audio signal in the time domain into the frequency domain to analyze its frequency components. This is very important for audio processing, especially in applications such as equalization and noise reduction. Generating a real-time spectrogram visualizes this frequency information in a graphical manner. Since audio signals are usually continuous and cannot be directly processed as a whole, the audio signal is divided into shorter frames (usually 20 milliseconds to 40 milliseconds). These frames are slightly overlapped during processing to reduce boundary effects and enable an intuitive observation of the energy distribution of the audio signal at different frequencies. A window function (such as a Hann window or a Hamming window) is applied to each frame to reduce the spectral leakage effect and ensure the clarity of the spectrum. The fast Fourier transform (FFT) is applied to each frame to transform the time-domain signal into the frequency domain, obtaining the spectral information of that frame. These spectral information represent the energy distribution of the audio signal at different frequencies. A spectrogram is a two-dimensional image, with the horizontal axis representing time, the vertical axis representing frequency, and the color or brightness representing the energy intensity at that time point and frequency. As the audio signal is continuously collected and processed, the spectrogram is dynamically updated to show the current changes in frequency components in real time.

[0042] Through the spectrogram, different frequency components in the audio signal can be visually observed. For example, the low-frequency region (such as below 100 Hz) may represent the bass part, while the high-frequency region (such as several kilohertz) may represent the treble or noise; the spectrogram can help identify noise or interference in the audio. For example, when a certain specific frequency band is unusually prominent in the spectrogram, it may indicate the presence of an interference signal; by observing the spectrogram, the equalizer parameters can be adjusted in real time. For example, if the low-frequency part is too strong, the gain of the low-frequency band can be reduced; if the high-frequency part is insufficient, the gain of the high-frequency band can be increased; during audio recording or transmission, the spectrogram can be used to diagnose audio problems, such as the absence or abnormal enhancement of certain frequency bands.

[0043] S30. Compare the real-time spectrum with the reference spectrum, and calculate the difference between each frequency band and the reference spectrum. The reference spectrum is an ideal sound pre-recorded or a target spectrum set in the system.

[0044] In this step, the spectra of the bass, midrange, and treble segments of the singer analyzed are compared with the reference audio (which may be a standard sound or a target sound), and the differences (deviations) in the frequency components of each frequency band from the reference audio are calculated. Here, the reference spectrum is an ideal sound pre-recorded or a target spectrum set in the system. The reference spectrum is an ideal frequency distribution diagram used to compare and adjust the spectrum of the actual audio signal to achieve the desired sound quality effect. The reference spectrum can come from various sources, specifically depending on the application scenario and the target. For example, a standardized spectrum, usually set according to a certain standard or specification; an ideal spectrum, an ideal spectrum shape set according to the ideal sound quality in a specific application scenario; the spectrum of a reference audio, using high-quality audio as the reference audio and taking its spectrum as the reference. The reference spectrum provides a clear target for audio processing, making the equalization adjustment more scientific and systematic, reducing the errors of subjective adjustment. By comparing with the reference spectrum, deficiencies in the audio signal, such as the absence or excessive strength of certain frequency bands, can be identified and corrected, thus optimizing the overall sound quality.

[0045] S40. According to the deviation of each frequency band, the gain setting of the equalizer is adjusted in real time, so that the microphone can automatically adjust the frequency response according to the voice characteristics of the singer in real time.

[0046] In this step, according to the deviation of each frequency band, the gain setting of the automatic equalizer is adjusted. For example, if the frequency response of the singer in the bass segment is weak, the gain of this frequency band can be increased, and vice versa. These adjustments are applied to the equalizer of the microphone, so that the microphone can automatically adjust the frequency response according to the voice characteristics of the singer in real time. Through continuous monitoring and adjustment, it can be ensured that the microphone always provides the best sound quality in different singing environments.

[0047] In a possible implementation manner, after the step of adjusting the gain setting of the equalizer in real time according to the deviation of each frequency band, the following steps are further included:

[0048] S401. Continuously monitor the adjusted audio output and check the effect of the equalization adjustment.

[0049] In this step, an audio analysis tool (such as a spectrum analyzer, RMS level detector, etc.) is used to continuously monitor the audio output signal, and key features of the adjusted audio are extracted, such as spectral distribution, volume level, signal-to-noise ratio (SNR), etc. These features can be used to evaluate the effect of equalization adjustment. Compare the adjusted audio spectrum with a predefined reference spectrum in real time; check whether the energy distribution in each frequency band matches the expected value of the reference spectrum, with particular attention to the energy distribution in the low, middle, and high frequencies; if the actual spectrum deviates from the reference spectrum, record these deviations as a basis for further adjustment. An automatic feedback mechanism can also be established in the audio processing system to enable the system to automatically adjust the equalization parameters according to the real-time monitoring results. For example, the system can set a threshold: if the energy in a certain frequency band continuously deviates from the reference value by more than the set threshold, the system will automatically adjust the equalizer to weaken or enhance the gain of that frequency band. After each automatic adjustment, the system will monitor the audio output again to verify whether the new equalization settings have improved the sound quality. Through continuous monitoring, the equalizer can dynamically adapt to changing audio inputs and environments, thus maintaining a stable sound quality effect.

[0050] S402. Collect the feedback from the singer and adjust the gain settings of the equalizer.

[0051] In this step, a physical control panel or foot switch can be provided, through which the singer can quickly submit feedback; or design a simple application or use a tablet computer to allow the singer to submit feedback through the touch screen; or integrate a voice recognition function in the recording software to allow the singer to submit feedback through voice (such as "increase low frequency" or "decrease high frequency"). While collecting the singer's feedback, record the current equalizer settings and audio output for correlation with the feedback. Classify the collected feedback to determine which frequency bands need to have their gains adjusted. For example, if the singer reports "insufficient bass", the gain of the low frequency band needs to be increased. Then, verify the singer's feedback in combination with audio monitoring data (such as a real-time spectrogram) to ensure that the feedback is consistent with the actual audio performance. Finally, adjust the gain settings of the equalizer according to the feedback. And loop the feedback and optimization process until the ideal effect is achieved.

[0052] In a possible implementation manner, in the step of continuously monitoring the adjusted audio output and checking the effect of equalization adjustment, it further includes:

[0053] Filter out environmental noise to ensure that the equalization adjustment focuses on the useful signal.

[0054] In this step, in combination with noise cancellation techniques, such as an adaptive filter, filter out environmental noise to ensure that the adaptive equalization focuses on the useful signal.

[0055] In a possible implementation manner, in the step of collecting and analyzing the audio signal of the singer, it includes:

[0056] S101. Collect and analyze the vocal range width of the singer, and mark the lowest and highest frequency ranges within which the singer can accurately produce sounds.

[0057] In this step, collect the singer's performance of "do, re, mi, fa, so, la, ti, do..." in the specified scale order, covering all pitches within the singer's vocal range. During the collection process, use high-quality microphones and recording equipment to record the singer's voice to ensure the clarity and accuracy of the recording. Perform time-frequency analysis on the recorded sound. Usually, use the Fourier transform (FFT) or short-time Fourier transform (STFT) to convert the audio signal into the frequency domain; analyze the frequency components of each scale, determine the upper and lower limits of the singer's vocal range, and calculate the width of the singer's vocal range. Based on the frequency analysis results, mark the lowest and highest frequency ranges within which the singer can accurately produce sounds, thereby determining the width of the vocal range.

[0058] S102. Collect and analyze the overtone characteristics of the singer. The overtone characteristics refer to other frequency components besides the fundamental frequency and are used to determine the timbre and texture of the sound.

[0059] In this step, collect a representative music segment sung by the singer and also record it using high-quality recording equipment. Perform time-frequency conversion analysis on the recorded music, pay attention to the main frequency and overtone components in the frequency spectrum, and record the overtone distribution and intensity in each frequency band (low, medium, high). Overtone refers to other frequency components besides the fundamental frequency, and they determine the timbre and texture of the sound. By analyzing the overtones, the unique characteristics of the singer's voice can be understood.

[0060] In a possible implementation manner, the step of performing time-frequency conversion on the collected audio signal to generate a real-time spectrogram includes:

[0061] Divide the audio signal into small window blocks, and the time of each window is 20 ms - 50 ms;

[0062] Apply the Fourier transform to each window to obtain spectral information.

[0063] In this step, first, determine the time length of each window, usually from 20 ms to 50 ms. This range is short enough to capture rapid changes in the audio signal and long enough to obtain good resolution in the frequency domain. For example, if the sampling rate is 44.1 kHz (common in audio processing), 20 ms corresponds to 882 sample points and 50 ms corresponds to 2205 sample points. Then, divide the audio signal into multiple small blocks according to the set window size. For each window, extract the corresponding sample data. Usually, the overlapping window technique is used, that is, there is a certain degree of overlap (e.g., 50% overlap) between each window to reduce window boundary effects and better capture transient changes. Before performing the Fourier transform on each window, apply a window function (such as Hanning window, Hamming window, or Blackman window) to reduce spectral leakage. The window function smooths the signal at the boundaries, reduces the spurious spectral components caused by truncation, multiplies the signal of each window by the selected window function to generate smooth signal data. Perform a fast Fourier transform (FFT) on the signal data after applying the window function to convert the time-domain signal into a frequency-domain signal. For each window, extract and store the spectral information, usually including the amplitude and phase of the frequency components. These spectral information can be further processed or visualized (such as plotting a spectrogram) to analyze the frequency characteristics and changes of the audio signal. Arrange the spectral information of each window into a time series to generate a spectrogram, with the vertical axis representing frequency, the horizontal axis representing time, and the color or brightness representing the energy intensity of each frequency component. By observing the spectrogram, analyze the change of the frequency components of the audio signal over time and identify specific audio features or events (such as notes, noise, speech segments, etc.).

[0064] In a possible implementation, in the step of adjusting the gain setting of the equalizer in real time according to the difference of each frequency band, the calculation method of the frequency band interpolation is D(f,t) = T(f) - |X(f,t)|, where D(f,t) represents the spectral difference at the current time t at the frequency f, and |X(f,t)| is the amplitude of the input signal spectrum.

[0065] In a possible implementation, the method of the gain setting is G(f,t) = αD(f,t) + Gprev(f,t), where α is an adjustment coefficient that controls the rate of gain adjustment, and Gprev(f,t) is the gain at the previous moment, used for smooth transition.

[0066] It is understandable that for the input audio signal x(t), time-frequency conversion needs to be performed first to convert it into a frequency-domain representation. A commonly used method is the short-time Fourier transform (STFT), X(f,t) = STFT{x(t)}, where X(f,t) is the spectrum at time t and f is the frequency. Next, the spectral difference is calculated. A target spectrum T(f) is set (which may be a preset ideal spectrum or a reference spectrum calculated based on historical data). For the current time t, the difference between the input signal spectrum and the target spectrum is calculated: D(f,t) = T(f) - |X(f,t)|, where D(f,t) represents the spectral difference at frequency f at the current time t, and |X(f,t)| is the amplitude of the input signal spectrum. Then, the gain adjustment is calculated. Based on the difference D(f,t), the gain adjustment amount G(f,t) for each frequency band is calculated. The gain adjustment is usually determined according to the magnitude of the difference: G(f,t) = αD(f,t) + Gprev(f,t), where α is the adjustment coefficient that controls the rate of gain adjustment, and Gprev(f,t) is the gain at the previous time, which is used for smooth transition. Then, the gain adjustment is applied to perform equalization adjustment on the original audio signal, usually implemented through a filter. Digital filters (such as IIR or FIR filters) can be used to implement the gain adjustment for each frequency band: Y(f,t) = G(f,t) × X(f,t), where Y(f,t) is the spectrum after applying the gain. Then, the adjusted spectrum Y(f,t) is converted back to a time-domain signal through the inverse short-time Fourier transform (ISTFT): y(t) = ISTFT{Y(f,t)}. Finally, feedback is performed based on the adjusted output signal y(t), and the above steps are repeated to continuously adjust the gain G(f,t) so that the output signal gradually approaches the reference spectrum T(f).

[0067] Exemplarily, assume that we are processing an audio signal with a sampling frequency of 44.1 kHz at a certain moment, and the goal is to achieve adaptive equalization. For simplicity, we only focus on three frequency bands: the low-frequency band (100 Hz), the middle-frequency band (1 kHz), and the high-frequency band (10 kHz). Assume that we have performed a short-time Fourier transform (STFT) on the audio signal and obtained the following spectral amplitudes: |X(100 Hz, t)| = 0.8; |X(1 kHz, t)| = 0.5; |X(10 kHz, t)| = 0.2; Assume that the amplitudes of the target spectrum (ideal spectrum) at these frequencies are: T(100 Hz) = 1.0; T(1 kHz) = 0.7; T(10 kHz) = 0.5; According to the formula, calculate the difference D(f, t) for each frequency band: For the low-frequency band (100 Hz): D(100 Hz, t) = 1.0 - 0.8 = 0.2; For the middle-frequency band (1 kHz): D(1 kHz, t) = 0.7 - 0.5 = 0.2; For the high-frequency band (10 kHz): D(10 kHz, t) = 0.5 - 0.2 = 0.3. Assume that the adjustment coefficient α = 0.5, and the gains Gprev(f, t) at the previous moment are all 1.0 (i.e., no adjustment). Then, the gain adjustment G(f, t) at the current moment is: For the low-frequency band (100 Hz): G(100 Hz, t) = 0.5×0.2 + 1.0 = 1.1; For the middle-frequency band (1 kHz): G(1 kHz, t) = 0.5×0.2 + 1.0 = 1.1; For the high-frequency band (10 kHz): G(10 kHz, t) = 0.5×0.3 + 1.0 = 1.15; Apply the calculated gains to the original spectrum: For the low-frequency band (100 Hz): Y(100 Hz, t) = 1.1×0.8 = 0.88; For the middle-frequency band (1 kHz): Y(1 kHz, t) = 1.1×0.5 = 0.55;

[0068] For the high-frequency band (10 kHz): Y(10 kHz, t) = 1.15×0.2 = 0.23; Finally, convert the adjusted spectrum Y(f, t) back to the time-domain signal y(t) through the inverse short-time Fourier transform (ISTFT). Assume that in this simplified example, we obtain the adjusted time-domain signal: y(t) = ISTFT{Y(f, t)}. In this example, by applying the gain adjustment, we make the output signal obtain a smaller gain boost in the low-frequency band (100 Hz) and the middle-frequency band (1 kHz), while obtaining a larger gain boost in the high-frequency band (10 kHz). Such adjustment can make the audio signal more balanced, closer to the target spectrum, and thus improve the sound quality.

[0069] In a second aspect, the present invention provides a microphone equalization adaptive adjustment device, including:

[0070] A collection module for collecting and analyzing the audio signal of a singer;

[0071] A conversion module for performing time-frequency conversion on the collected audio signal to generate a real-time spectrogram;

[0072] A calculation gain module for comparing the difference between the spectrum of the current audio and the reference spectrum and calculating the frequency band that needs to be adjusted;

[0073] An adjustment gain module for adjusting the gain setting of the equalizer in real time.

[0074] Those skilled in the art should understand that the division of each module in the embodiment is only a division of logical functions. In actual application, they can be fully or partially integrated into one or more actual carriers, and these modules can all be implemented in the form of software called by a processing unit, or all be implemented in the form of hardware, or be implemented in the form of a combination of software and hardware. It should be noted that each module in the microphone equalization adaptive adjustment device in this embodiment corresponds one by one to each step in the microphone equalization adaptive adjustment method in the foregoing embodiment. Therefore, the specific implementation manner of this embodiment can refer to the implementation manner of the foregoing microphone equalization adaptive adjustment method, which will not be elaborated here.

[0075] In a third aspect, the present invention provides a device, including: a processor and a memory, the memory is coupled to the processor, the memory is used to store computer program code, the computer program code includes computer instructions, and when the processor reads the computer instructions from the memory, so that the electronic device executes the microphone equalization adaptive adjustment method as described in the first aspect or any possible implementation manner in the first aspect.

[0076] In some embodiments, the computer-readable storage medium may be a memory such as FRAM, ROM, PROM, EPROM, EEPROM, flash memory, magnetic surface memory, optical disc, or CD-ROM; or it may be various devices including one or any combination of the foregoing memories. The computer may be various computing devices including smart terminals and servers.

[0077] In some embodiments, the executable instructions may be in the form of a program, software, software module, script, or code, and may be written in any form of programming language (including compiled or interpreted languages, or declarative or procedural languages), and may be deployed in any form, including being deployed as an independent program or being deployed as a module, component, subroutine, or other unit suitable for use in a computing environment.

[0078] By way of example, the executable instructions may or may not correspond to a file in a file system, may be stored as part of a file that holds other programs or data, for example, in one or more scripts stored in a HyperText Markup Language (HTML) document, stored in a single file dedicated to the program being discussed, or stored in multiple cooperating files (e.g., files that store one or more modules, subroutines, or portions of code).

[0079] By way of example, the executable instructions may be deployed to execute on one computing device, or on multiple computing devices located at one site, or alternatively, on multiple computing devices distributed across multiple sites and interconnected by a communication network.

[0080] The foregoing are only preferred embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application shall be included within the protection scope of the present application.

Claims

1. A method for adaptively adjusting the microphone equalization, characterized in that Including: Collect and analyze the audio signal of the singer; Perform time-frequency conversion on the collected audio signal to generate a real-time spectrogram; Compare the real-time spectrum with the reference spectrum, and calculate the difference between each frequency band and the reference spectrum. The reference spectrum is an ideal sound pre-recorded or a target spectrum set in the system; According to the difference of each frequency band, adjust the gain setting of the equalizer in real time, so that the microphone can automatically adjust the frequency response according to the voice characteristics of the singer in real time; Among them, in the step of collecting and analyzing the audio signal of the singer, it includes: Collect and analyze the vocal range width of the singer, and mark the lowest and highest frequency ranges where the singer can accurately produce sound; Collect and analyze the overtone characteristics of the singer. The overtone characteristics refer to other frequency components except the fundamental frequency, which are used to determine the timbre and texture of the sound; In the step of adjusting the gain setting of the equalizer in real time according to the difference of each frequency band, the calculation method of the frequency band interpolation is D(f,t) = T(f) - ∣X(f,t)∣, where D(f,t) represents the spectrum difference at frequency f at the current time t, and ∣X(f,t)∣ is the amplitude of the input signal spectrum; the method of gain setting is G(f,t) = αD(f,t) + Gprev(f,t), where α is the adjustment coefficient that controls the rate of gain adjustment, and Gprev(f,t) is the gain at the previous moment, which is used for smooth transition.

2. The method for adaptively adjusting the microphone equalization according to claim 1, wherein After the step of adjusting the gain setting of the equalizer in real time according to the difference of each frequency band, it further includes: Continuously monitor the adjusted audio output and check the effect of the equalization adjustment; Collect the feedback of the singer and adjust the gain setting of the equalizer.

3. The microphone equalization adaptive adjustment method according to claim 2, characterized in that In the step of continuously monitoring the adjusted audio output and checking the effect of the equalization adjustment, it further includes: Filter out environmental noise to ensure that the equalization adjustment focuses on useful signals.

4. The method for adaptively adjusting the microphone equalization according to claim 1, wherein The step of performing time-frequency conversion on the collected audio signal to generate a real-time spectrogram includes: Divide the audio signal into small window blocks, and the time of each window is 20ms - 50ms; Apply Fourier transform to each window to obtain spectrum information.

5. An adaptive adjustment device for microphone equalization, characterized in that, Including: A collection module for collecting and analyzing the audio signal of the singer; A conversion module for performing time-frequency conversion on the collected audio signal to generate a real-time spectrogram; A calculation gain module for comparing the spectrum of the current audio with the reference spectrum and calculating the frequency band that needs to be adjusted; An adjustment gain module for adjusting the gain setting of the equalizer in real time; Among them, the device is also used to collect and analyze the vocal range width of the singer, and mark the lowest and highest frequency ranges where the singer can accurately produce sound; Collect and analyze the overtone characteristics of the singer. The overtone characteristics refer to other frequency components except the fundamental frequency, which are used to determine the timbre and texture of the sound; The calculation method of the frequency band interpolation is D(f,t) = T(f) - |X(f,t)|, where D(f,t) represents the spectral difference at the current time t on the frequency f, and |X(f,t)| is the amplitude of the input signal spectrum; the method of gain setting is G(f,t) = αD(f,t) + Gprev(f,t), where α is an adjustment coefficient that controls the rate of gain adjustment, and Gprev(f,t) is the gain at the previous time, which is used for smooth transition.

6. A device, characterized in that, It includes: A processor and a memory, the memory is coupled to the processor, the memory is used to store computer program code, the computer program code includes computer instructions, when the processor reads the computer instructions from the memory, so that the electronic device executes the microphone equalization adaptive adjustment method according to any one of claims 1-4.

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