Low-Noise Wireless Microphone Based on MEMS Audio Sensor and Filtering Method

Abstract

This application relates to the technical field of voice signal processing, specifically to a low-noise wireless microphone and a filtering method based on a MEMS audio sensor. The method includes: determining the waveform symmetry degree of each voice signal segment based on the correlation between all peaks and all valleys, and the change trend of all peaks in the time domain of each voice signal segment; obtaining the spectrum of each voice signal segment and extracting all peaks in the spectrum, and determining the spike significance value of each voice signal segment based on the distribution degree of all peaks and the difference between the maximum peak and all the remaining peaks; performing noise reduction on the voice signal in the microphone based on the waveform symmetry degree and the spike significance value. This application aims to efficiently identify and specifically eliminate howling noise, and improve the quality of the audio output by the wireless microphone.

Description

Technical Field

[0001] This application relates to the technical field of voice signal processing, and specifically to a low-noise wireless microphone and filtering method based on a MEMS audio sensor. Background Art

[0002] A MEMS audio sensor is an audio electroacoustic conversion component produced by using advanced semiconductor manufacturing processes of MEMS (Micro-Electro-Mechanical Systems). It has the advantages of small size, low cost, high sensitivity, low power consumption, etc., making it very suitable for use in portable devices such as wireless microphones. A wireless microphone is an audio device widely used in occasions such as stage performances, conference speeches, public speeches, etc. It uses wireless technologies (such as radio frequency, Bluetooth, etc.) to transmit audio signals, without the need for physical connection, providing greater flexibility and convenience.

[0003] Low-noise wireless microphones usually use filtering technologies to perform noise reduction processing on voice signals collected by MEMS audio sensors, so as to reduce the interference of environmental noise and improve the clarity and quality of voice signals. In most cases, existing wireless microphones use a filtering method to perform noise reduction processing on the collected voice signals. This method is not targeted at the noise in the voice signals, and it is very easy to cause certain types of noise in the voice signals to not be effectively filtered out, such as the howling noise that often appears in occasions such as stage performances, conference speeches, public speeches, etc., reducing the quality of the audio output by the wireless microphone. Summary of the Invention

[0004] In order to solve the above technical problems, the purpose of this application is to provide a low-noise wireless microphone and filtering method based on a MEMS audio sensor, and the technical solutions adopted are specifically as follows:

[0005] In a first aspect, an embodiment of this application provides a filtering method for a low-noise wireless microphone based on a MEMS audio sensor. The method includes the following steps:

[0006] Obtain the voice signal in the low-noise wireless microphone, and divide the voice signal into multiple voice signal segments;

[0007] Extract all the peaks and valleys in the time domain of each voice signal segment. Based on the correlation between all the peaks and all the valleys, determine the fluctuation correlation degree of each voice signal segment; Based on the change trend of all the peaks in each voice signal segment, determine the trend fluctuation degree of each voice signal segment, and combine the fluctuation correlation degree to determine the waveform symmetry degree of each voice signal segment;

[0008] Obtain the spectra of each voice signal segment, extract all the peaks in the spectra, and determine the first sharpness of each voice signal segment based on the distribution degree of all the peaks; determine the second sharpness of each voice signal segment based on the difference between the maximum peak and all the other peaks in the spectra of each voice signal segment, and combine the first sharpness to determine the peak significance value of each voice signal segment;

[0009] Based on the waveform symmetry and the peak significance value, determine the noise coefficient of each voice signal segment, and perform noise reduction on the voice signals in the low-noise wireless microphone.

[0010] Preferably, the fluctuation correlation degree of each voice signal segment is the Pearson correlation coefficient between all the peaks and all the valleys in the time domain of each voice signal segment.

[0011] Preferably, the method for determining the trend fluctuation degree of each voice signal segment is as follows:

[0012] Take all the peaks in the time domain of each voice signal segment as the input of the trend test algorithm, output the trend statistic of each voice signal segment, and use it as the trend fluctuation degree of each voice signal segment.

[0013] Preferably, the expression of the waveform symmetry of each voice signal segment is: ; where represents the waveform symmetry of voice signal segment i; represents the trend fluctuation degree of voice signal segment i; represents the fluctuation correlation degree of voice signal segment i; exp( ) represents the exponential function with the natural constant as the base; norm[ ] represents the normalization function.

[0014] Preferably, the first sharpness of each voice signal segment is the kurtosis of all the peaks in the spectrum of each voice signal segment.

[0015] Preferably, the second sharpness of each voice signal segment is the average value of the difference between the maximum peak and all the other peaks in the spectrum of each voice signal segment.

[0016] Preferably, the expression of the peak significance value of each voice signal segment is: ; where represents the peak significance value of voice signal segment i; represents the first sharpness of voice signal segment i; represents the second sharpness of voice signal segment i; norm( ) represents the normalization function.

[0017] Preferably, the expression of the noise coefficient of each voice signal segment is: ; represents the noise coefficient of voice signal segment i; It represents the waveform symmetry of the voice signal segment i; It represents the peak significant value of the voice signal segment i.

[0018] Preferably, the noise reduction of the voice signal in the low-noise wireless microphone includes:

[0019] Taking the noise coefficients of all voice signal segments as the input of the threshold segmentation algorithm, and outputting the segmentation threshold;

[0020] If the noise coefficient of a voice signal segment is greater than the segmentation threshold, taking it as the input of the Kalman filtering algorithm and outputting the noise-reduced voice signal segment; on the contrary, if the noise coefficient of a voice signal segment is less than or equal to the segmentation threshold, taking this voice signal segment as the input of the LMS adaptive filtering algorithm and outputting the noise-reduced voice signal segment.

[0021] In a second aspect, an embodiment of the present application further provides a low-noise wireless microphone based on a MEMS audio sensor, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the steps of the above-mentioned low-noise wireless microphone filtering method based on a MEMS audio sensor are implemented.

[0022] The present application has at least the following beneficial effects:

[0023] In this application, the voice signal in the microphone is divided into multiple voice signal segments, and the correlation of the change trends of the wave peaks and wave valleys in each voice signal segment, as well as the change trend of the peak value, are analyzed to construct the waveform symmetry degree. The beneficial effect is that it can effectively evaluate whether the time-domain waveform of the voice signal segment presents a horn-shaped waveform with an opening to the right, which is a typical characteristic of howling noise, improving the distinguishability between the time-domain waveform characteristics of the human voice and howling noise in the voice signal segment, accurately identifying possible howling segments, and performing targeted noise reduction, thereby improving the quality of the audio output by the wireless microphone; by analyzing the change trend and steepness of the peak value of the voice signal segment in the frequency domain, this application constructs the peak significance value, and its beneficial effect is that it can effectively identify the peaks in the frequency domain, improving the recognition accuracy of the voice signal segment containing howling noise, and thus more accurately locating the howling noise; by comprehensively analyzing the analysis results of the voice signal segment in the time domain and the frequency domain, this application constructs the noise coefficient, and its beneficial effect is that it can accurately identify the voice signal segment containing howling noise, and adopt different filtering strategies for the voice signal segment containing howling noise and the remaining voice signal segments, thereby optimizing the filtering method and improving the quality of the audio output by the wireless microphone; by comprehensively analyzing the characteristics of the voice signal in the time domain and the frequency domain, this application efficiently identifies and targets the elimination of howling noise. By performing Kalman filtering on the voice signal segment containing howling noise and performing LMS adaptive filtering on the voice signal segment without howling noise, it reduces the possibility that the howling noise in the collected voice signal cannot be effectively filtered, improving the quality of the audio output by the wireless microphone. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0025] Figure 1 It is a flowchart of the steps of a low-noise wireless microphone filtering method based on a MEMS audio sensor provided by an embodiment of the present application;

[0026] Figure 2 It is a time-domain waveform diagram of howling noise provided by an embodiment of the present application;

[0027] Figure 3 It is a spectrogram of howling noise provided by an embodiment of the present application;

[0028] Figure 4 It is a schematic diagram of the noise coefficient extraction process provided by an embodiment of the present application;

[0029] Figure 5 The time-domain waveform diagram after filtering out the howling noise provided by an embodiment of the present application. Specific embodiments

[0030] In order to further elaborate on the technical means and effects adopted by the present application to achieve the predetermined invention purpose, the following combines the accompanying drawings and preferred embodiments to detail the specific embodiments, structures, features and their effects of the low-noise wireless microphone and filtering method based on MEMS audio sensors proposed according to the present application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0031] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which this application belongs.

[0032] The following specifically describes the specific solutions of the low-noise wireless microphone and filtering method based on MEMS audio sensors provided by the present application with reference to the accompanying drawings.

[0033] Please refer to Figure 1 , which shows the step flowchart of the low-noise wireless microphone filtering method based on MEMS audio sensors provided by an embodiment of the present application. The method includes the following steps:

[0034] Step S1: Obtain the voice signal in the low-noise wireless microphone and divide the voice signal into multiple voice signal segments.

[0035] During large-scale events such as stage performances or public speeches, the natural voices of performers may not be clearly heard by everyone. Therefore, the voices are amplified by using microphones and sound equipment to ensure that the audience in every corner can obtain a good auditory experience. However, when the microphone and the sound system are used simultaneously, the sound emitted by the sound system can be transmitted to the microphone through space, which may cause a sound feedback phenomenon, that is, howling. Howling will affect the quality and stability of the sound. Therefore, in order to ensure the quality of the audio output by the wireless microphone, it is necessary to filter out howling-like noises.

[0036] The low-noise wireless microphone in this embodiment is composed of an input device, a transmitter, a receiver, and a power supply. The input device is composed of a MEMS audio sensor and a data processing unit. The MEMS audio sensor is used to collect the voice signal in the microphone and transmit it to the data processing unit, where the sampling frequency is set to f.

[0037] It should be noted that the value of the sampling frequency f is set artificially. In this embodiment, the value of the sampling frequency is 44.1 KHz. The implementer can also set it according to the specific situation, and this embodiment does not make special restrictions.

[0038] Furthermore, in order to ensure that the amplitudes in the voice signal are all on the same magnitude level, the amplitudes of the voice signal are normalized. There are many common normalization methods. In this embodiment, the maximum-minimum normalization method is used to normalize the voice signal. The implementer can also combine other normalization methods such as the z-score normalization method. Regarding the selection of the normalization method, this embodiment does not make special restrictions.

[0039] Among them, the maximum-minimum normalization method is a well-known technology, and the specific process of normalizing the data will not be elaborated here.

[0040] Since the voice signal may contain whistling, in order to specifically suppress the whistling in the voice signal, the voice signal is divided into multiple voice signal segments.

[0041] It should be noted that there are many common segmentation algorithms. In this embodiment, the voice signal is used as the input of the voice segmentation algorithm VAD, and multiple voice signal segments are output to realize the segmentation of the voice signal. The implementer can also adopt other segmentation methods according to the specific situation. Regarding the selection of the segmentation algorithm, this embodiment does not make special restrictions.

[0042] Among them, the semantic segmentation algorithm VAD is a well-known technology, and its specific principle will not be elaborated here.

[0043] Step S2: Extract all the peaks and valleys in the time domain of each voice signal segment, determine the fluctuation correlation degree of each voice signal segment based on the correlation between all the peaks and all the valleys; determine the trend fluctuation degree of each voice signal segment based on the change trend of all the peaks in each voice signal segment, and combine the fluctuation correlation degree to determine the waveform symmetry degree of each voice signal segment.

[0044] In occasions such as stage performances, conference speeches, and public speeches, the ambient noise usually includes sounds from various different sources, such as background conversations, air conditioner sounds, etc. The sound sources of these sounds are usually relatively dispersed and far from the wireless microphone. When the user uses the wireless microphone to give a speech, the sound source of the human voice is relatively concentrated and close to the wireless microphone, and the whistling noise generated in the wireless microphone is usually a very loud and sharp whistling sound, making the human voice and the whistling noise in the collected voice signal have a higher volume than the other types of ambient noise in the voice signal.

[0045] Generally, the speech signal of human voice usually does not have an obvious regular waveform in the time domain. This is because the human voice is a complex signal composed of multiple frequency components, including voiceless and voiced sounds. The waveform of the human voice changes with the speaking content and intonation, without a fixed pattern. While the howling noise in a wireless microphone usually presents a signal waveform in the shape of a flared horn opening to the right in the time domain. This is because howling is usually caused by acoustic feedback. When the sound emitted from the speaker is picked up by the wireless microphone again, amplified by the amplifier and then emitted from the speaker again, and so on in a cycle. Each cycle will increase the sound intensity of a specific frequency, forming positive feedback, resulting in an increasing volume and finally forming howling. This process is manifested in the time domain as the amplitude of the signal gradually increasing in a symmetric form.

[0046] Therefore, based on the differences in the waveform characteristics of the human voice and howling signals in the time domain, analyze the variation law characteristics of each speech signal segment, so as to screen out the speech signal segments that may contain howling noise and filter out the howling noise in the speech signal. Specifically:

[0047] (1) Extract all the peaks and valleys in the time domain of each speech signal segment. Among them, there are many common peak and valley extraction algorithms. In this embodiment, the second-order difference recognition algorithm of wave peaks and valleys is used to obtain all the peaks and valleys in the time domain of the speech signal segment. Implementers can also use other peak and valley extraction algorithms. The selection of peak and valley extraction methods is not particularly limited in this embodiment.

[0048] Among them, the second-order difference recognition algorithm of wave peaks and valleys is a well-known technology, and the specific process of extracting the peaks and valleys in the time domain of the speech signal segment will not be elaborated here.

[0049] Preferably, the time-domain waveform diagram of the howling noise provided in this embodiment is as Figure 2 shown.

[0050] (2) Further, take the Pearson correlation coefficient between all the peaks and all the valleys in the time domain of each speech signal segment as the fluctuation correlation degree of each speech signal segment;

[0051] Among them, the calculation steps of the Pearson correlation coefficient are a well-known technology, and its specific calculation process will not be elaborated here.

[0052] (3) Further, take all the peaks in the time domain of each speech signal segment as the input of the trend test algorithm, output the trend statistic of each speech signal segment, and use it as the trend fluctuation degree of each speech signal segment;

[0053] It should be noted that there are many common trend test algorithms. In this embodiment, the Mann-Kendall trend test algorithm is used. Implementers can also use the Kendall trend test algorithm to test the change trend of the peaks. The selection of trend test algorithms is not particularly limited in this embodiment.

[0054] Among them, the Mann-Kendall trend test algorithm is a well-known technology, and the specific process of testing the change trend of data will not be elaborated here.

[0055] (4) Further, based on the trend fluctuation degree and fluctuation correlation degree of each voice signal segment, determine the waveform symmetry degree of each voice signal segment, specifically:

[0056] The waveform symmetry degree of voice signal segment i The expression of: ; In the formula, represents the trend fluctuation degree of voice signal segment i; represents the fluctuation correlation degree of i; exp( ) represents the exponential function with the natural constant as the base; norm[ ] represents the normalization function.

[0057] It can be understood from the waveform symmetry degree of each voice signal segment that the more the signals between the upper and lower halves of the time domain horizontal axis in the voice signal segment show opposite trends over time, the smaller the fluctuation correlation degree, indicating that the signals in the upper and lower halves of the time domain horizontal axis of the voice signal are more symmetric, that is, showing the characteristics of horn symmetry, then it is more likely to be a voice signal segment containing howling noise; and the more the amplitude of the signal in the upper half of the time domain horizontal axis in the voice signal segment shows an increasing trend over time, that is, the greater the trend fluctuation degree, the more the amplitude of the voice signal segment shows a changing trend of gradually increasing in a symmetric form in the time domain, then the time domain waveform of the voice signal segment is more likely to be in the shape of a horn mouth opening to the right, that is, the greater the waveform symmetry degree, and the corresponding signal segment is more likely to be the voice signal segment where the howling occurs; on the contrary, the greater the fluctuation correlation degree of the voice signal segment and the smaller the trend fluctuation degree, the smaller the waveform symmetry degree, and the less likely the time domain waveform of the voice signal segment is in the shape of a horn mouth opening to the right, and the corresponding signal segment is less likely to be a voice signal segment containing howling noise.

[0058] Step S3: Obtain the spectrum of each voice signal segment, extract all the peaks in the spectrum, and determine the first sharpness of each voice signal segment based on the distribution degree of all the peaks; determine the second sharpness of each voice signal segment based on the difference between the maximum peak and the rest of all the peaks in the spectrum of each voice signal segment, and combine the first sharpness to determine the peak significance value of each voice signal segment.

[0059] During the formation of howling noise in a wireless microphone, it usually increases the sound intensity of a specific frequency, forms positive feedback, causes the volume to become larger and larger, and thus appears as a harsh whistling sound in the auditory sense, making the howling noise in the collected voice signal have a more single and large frequency amplitude compared to human voices in the frequency domain, and then presenting a frequency domain waveform with obvious peaks.

[0060] Therefore, by analyzing the distribution characteristics of the voice signal in the frequency domain, to further determine the voice signal segment where the howling may be located, and perform noise reduction processing on it, so as to improve the quality of the audio output by the wireless microphone. Specifically:

[0061] (1) Take each voice signal segment as the input of the time-frequency conversion algorithm, and output the spectrum of each voice signal segment;

[0062] It should be understood that there are many common time-frequency conversion algorithms. In this embodiment, the fast Fourier transform is used to obtain the spectrum of each voice signal segment. Implementers can also use other time-frequency conversion algorithms such as wavelet transform. There is no special limitation on the selection of the time-frequency conversion algorithm in this embodiment.

[0063] Among them, the fast Fourier transform is a well-known technology in the field of signal processing, and the specific process of converting the time-domain signal to the frequency domain will not be elaborated here.

[0064] Preferably, the spectrogram of the howling noise provided in this embodiment is as Figure 3 shown.

[0065] (2) Further, extract all the peaks in the spectrum of each voice signal segment, and take the kurtosis of all the peaks in the spectrum of each voice signal segment as the first sharpness of each voice signal segment;

[0066] Among them, the calculation steps of kurtosis are well-known technologies, and the specific calculation process will not be elaborated here.

[0067] (3) Further, take the average of the difference between the maximum peak and all the other peaks in the spectrum of each voice signal segment as the second sharpness of each voice signal segment;

[0068] It should be noted that there are many methods to measure the difference between data. In this embodiment, by calculating the absolute value of the difference between the maximum peak and the other peaks in the spectrum of each voice signal segment, as a method to measure the difference between the maximum peak and the other peaks, implementers can also combine specific situations and use other methods to measure the difference between data such as ratios. There is no special limitation on the selection of the method to measure the difference between data in this embodiment.

[0069] (4) Further, based on the first sharpness and the second sharpness, determine the peak significance value of the voice signal segment, so as to judge the voice signal segment where the howling is located. Specifically:

[0070] The peak significance value of the voice signal segment i is expressed as: ; In the formula, represents the first sharpness of the voice signal segment i; represents the second sharpness of the voice signal segment i; norm( ) represents the normalization function.

[0071] It can be understood from the peak significance values of each voice signal segment that if the steepness of all peaks on the spectrum is greater, that is, the first sharpness is greater, and the difference between the maximum peak and the remaining peaks of the voice signal segment in its frequency domain is greater, that is, the second sharpness is greater, then the frequency domain waveform with obvious peaks is more likely to appear in the spectrum of the voice signal segment, that is, the peak significance value is greater, indicating that the corresponding voice signal segment is more likely to be a voice signal segment containing whistling noise; conversely, if the steepness of all peaks on the spectrum is smaller, that is, the first sharpness is smaller, and the difference between the maximum peak and the remaining peaks of the voice signal segment in its frequency domain is smaller, that is, the second sharpness is smaller, then the frequency domain waveform with obvious peaks is less likely to appear in the spectrum of the voice signal segment, that is, the peak significance value is smaller, indicating that the corresponding voice signal segment is less likely to be a voice signal segment containing whistling noise.

[0072] Step S4: Based on the waveform symmetry and peak significance values of each voice signal segment, determine the noise coefficient of each voice signal segment, and perform noise reduction on the voice signals in the low-noise wireless microphone.

[0073] Combining the waveform symmetry and peak significance values, determine the noise coefficient to accurately determine the voice signal segment containing whistling noise and perform noise reduction processing on this voice signal segment. Specifically:

[0074] The noise coefficient of voice signal segment i The expression is: ; represents the waveform symmetry of voice signal segment i; represents the peak significance value of voice signal segment i.

[0075] Preferably, the schematic diagram of the noise coefficient extraction process provided in this embodiment is as Figure 4 shown.

[0076] Furthermore, take the noise coefficients of all voice signal segments as the input of the threshold segmentation algorithm, and output the segmentation threshold;

[0077] If the noise coefficient of the voice signal segment is greater than the segmentation threshold, use it as the input of the Kalman filter algorithm and output the noise-reduced voice signal segment. On the contrary, if the noise coefficient of the voice signal segment is less than or equal to the segmentation threshold, use this voice signal segment as the input of the LMS adaptive filter algorithm and output the noise-reduced voice signal segment.

[0078] It should be noted that there are many common threshold segmentation algorithms. In this embodiment, the maximum inter-class variance algorithm is used. Implementers can also use other threshold segmentation algorithms according to specific situations. Regarding the selection of the threshold segmentation algorithm, this embodiment does not make special restrictions.

[0079] Among them, the maximum inter-class variance algorithm is a well-known technology, and its specific principle will not be elaborated here.

[0080] It should be noted that since whistling is a specific and periodic interference, and its change has a certain regularity, when dealing with whistling with regular changes, the Kalman filter algorithm is usually used to filter and reduce the noise. Because the Kalman filter algorithm is mainly used for state estimation of linear Gaussian systems, it can provide the optimal estimation of the state, enabling it to effectively identify and predict the frequency components of whistling. Through accurate prediction, the Kalman filter algorithm can specifically eliminate whistling without affecting other normal speech parts, thus maintaining the clarity and audibility of the speech signal.

[0081] For other parts of the speech signal, there may be other unexpected sounds such as environmental noise and device noise. They are usually time-varying and may have non-linear characteristics. The LMS adaptive filter algorithm is usually used for situations where the system model is unknown or changing. Therefore, the LMS adaptive filter algorithm is adopted to adjust the filter parameters in real time to adapt to the changing noise and interference, thereby effectively suppressing the noise and improving the quality of the audio output by the wireless microphone.

[0082] Preferably, the time-domain waveform diagram of the filtered whistling noise provided in this embodiment is as Figure 5 shown.

[0083] Based on the same inventive concept as the above method, the embodiment of the present application also provides a low-noise wireless microphone based on a MEMS audio sensor, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of any one of the above methods of the low-noise wireless microphone filtering method based on a MEMS audio sensor.

[0084] It should be noted that: the above sequence of embodiments of the present application is only for description and does not represent the superiority or inferiority of the embodiments. And the above describes specific embodiments of this specification. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0085] Each embodiment in this specification is described in a progressive manner. The same or similar parts among the embodiments can be referred to each other, and the key points of each embodiment are the differences from other embodiments.

[0086] The above are only the preferred embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the principle of the present application shall be included in the protection scope of the present application.

Claims

1. A low-noise wireless microphone filtering method based on a MEMS audio sensor, characterized in that: The method comprises the following steps: Acquire a speech signal from a low-noise wireless microphone, and divide the speech signal into a plurality of speech signal segments; Extract all peaks and valleys in the time domain of each speech signal segment, and determine the fluctuation correlation of each speech signal segment based on the correlation between all peaks and all valleys; determine the trend fluctuation of each speech signal segment based on the change trend of all peaks in each speech signal segment, and determine the waveform symmetry of each speech signal segment in combination with the fluctuation correlation; Acquire the spectrum of each speech signal segment, extract all peaks in the spectrum, and determine the first sharpness of each speech signal segment based on the distribution degree of all peaks; determine the second sharpness of each speech signal segment based on the difference between the maximum peak and all other peaks in the spectrum of each speech signal segment, and determine the peak significance value of each speech signal segment in combination with the first sharpness; Based on the waveform symmetry and the peak significance value, the noise coefficient of each speech signal segment is determined, and the speech signal in the low-noise wireless microphone is denoised.

2. The low-noise wireless microphone filtering method based on MEMS audio sensor according to claim 1, characterized in that: The fluctuation correlation of each speech signal segment is the Pearson correlation coefficient between all peak values ​​and all valley values ​​in the time domain of each speech signal segment.

3. The low-noise wireless microphone filtering method based on MEMS audio sensor as claimed in claim 1, characterized in that: The method for determining the trend fluctuation of each speech signal segment is as follows: All peak values ​​in the time domain of each speech signal segment are used as input of the trend detection algorithm, and the trend statistics of each speech signal segment are output as the trend fluctuation degree of each speech signal segment.

4. The low-noise wireless microphone filtering method based on MEMS audio sensor as claimed in claim 1, characterized in that: The expression of the waveform symmetry of each speech signal segment is: ; In the formula, Indicates the waveform symmetry of speech signal segment i; Indicates the trend fluctuation of speech signal segment i; represents the fluctuation correlation of speech signal segment i; exp( ) represents an exponential function with a natural constant as the base; norm[ ] represents a normalization function.

5. The low-noise wireless microphone filtering method based on MEMS audio sensor as claimed in claim 1, characterized in that: The first sharpness of each speech signal segment is the kurtosis of all peaks in the frequency spectrum of each speech signal segment.

6. The low-noise wireless microphone filtering method based on MEMS audio sensor as claimed in claim 1, characterized in that: The second sharpness of each speech signal segment is the average of the difference between the maximum peak and all other peaks in the frequency spectrum of each speech signal segment.

7. The low-noise wireless microphone filtering method based on MEMS audio sensor as claimed in claim 1, characterized in that: The expression of the peak significance value of each speech signal segment is: ; In the formula, represents the peak significance value of speech signal segment i; represents the first sharpness of speech signal segment i; represents the second sharpness of speech signal segment i; norm() represents the normalization function.

8. The low-noise wireless microphone filtering method based on MEMS audio sensor as claimed in claim 1, characterized in that: The expression of the noise coefficient of each speech signal segment is: ; represents the noise factor of speech signal segment i; Indicates the waveform symmetry of speech signal segment i; Represents the peak significance value of speech signal segment i.

9. The low-noise wireless microphone filtering method based on MEMS audio sensor as claimed in claim 1, characterized in that: The method of reducing the noise of a voice signal in a low-noise wireless microphone comprises: The noise coefficients of all speech signal segments are used as the input of the threshold segmentation algorithm, and the segmentation threshold is output; If the noise coefficient of the speech signal segment is greater than the segmentation threshold, it is used as the input of the Kalman filtering algorithm and the denoised speech signal segment is output. Conversely, if the noise coefficient of the speech signal segment is less than or equal to the segmentation threshold, the speech signal segment is used as the input of the LMS adaptive filtering algorithm and the denoised speech signal segment is output.

10. A low-noise wireless microphone based on a MEMS audio sensor, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the low-noise wireless microphone filtering method based on the MEMS audio sensor are implemented as described in any one of claims 1 to 9.

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