Wind noise resistance method of OWS ear clip type earphone, electronic equipment and readable storage medium

Abstract

The invention discloses a wind noise resistance method of an OWS ear clip type earphone, electronic equipment and a readable storage medium, and the wind noise resistance method of the OWS ear clip type earphone can enable a voice signal and a wind noise signal to have an obvious distinction degree, reduces the influence of wind noise on the earphone in a use process, and facilitates the use of a user in a wind noise scene. And the content of the voice call can be clearly and accurately heard, so that the definition of the earphone during call and music listening is higher, and the use experience of the user is better.

Description

Technical Field

[0001] The invention relates to the field of earphones, and in particular to an anti-wind noise method for OWS ear-clip earphones, an electronic device and a readable storage medium. Background Art

[0002] OWS headphones (Open Wearable Stereo) are open-ear headphones that provide a different wearing experience from traditional in-ear headphones, especially when exercising, allowing users to sense external sounds and improve safety. However, OWS headphones also face some technical challenges in design, among which wind noise is a more prominent problem.

[0003] Wind noise is the non-speech wind noise signal captured by the headset microphone in a windy environment, which is more obvious when moving at high speed or outdoors. Due to its open design, the microphone of OWS headset is closer to the external environment, so it is more susceptible to interference from wind noise. The wind noise during use seriously affects the clarity of calls and listening to music, making the user experience poor. Therefore, it is necessary to study a new technical solution to solve the above problem. Summary of the invention

[0004] In view of this, the present invention aims at the deficiencies in the prior art, and its main purpose is to provide an anti-wind noise method, electronic device and readable storage medium for OWS ear-clip headphones, which can effectively solve the problems of the existing OWS headphones being easily disturbed by wind noise during use, poor clarity in calls and listening to music, and poor user experience.

[0005] To achieve the above purpose, the present invention adopts the following technical solutions:

[0006] An anti-wind noise method for an OWS ear clip headset, the OWS ear clip headset comprising a feedforward microphone, an auxiliary microphone and a call microphone, the feedforward microphone is used to directly obtain a wind noise signal, the auxiliary microphone is used to collect a wind noise signal that has undergone physical noise reduction, and the call microphone is used to collect a voice signal during a call; the anti-wind noise method comprises the following steps:

[0007] Performing Fourier transform on the first wind noise signal collected by the feedforward microphone and the second wind noise signal collected by the auxiliary microphone respectively to obtain the frequency domain of the first wind noise signal and the frequency domain of the second wind noise signal;

[0008] Determine, according to the frequency domain of the first wind noise signal and the frequency domain of the second wind noise signal, a first power spectral density of the first wind noise signal and a second power spectral density of the second wind noise signal;

[0009] Marking a signal segment in the first wind noise signal where the first power spectrum density is greater than a preset first threshold as a first designated wind noise signal, and marking a signal segment in the second wind noise signal where the second power spectrum density is less than a preset second threshold as a second designated wind noise signal;

[0010] The first designated wind noise signal and the second designated wind noise signal are analyzed respectively, and when the peak values ​​of the frequency domains of the first designated wind noise signal and the second designated wind noise signal are greater than a preset frequency domain threshold, the corresponding peak points are respectively used as the main frequency components, and the frequencies of the first designated wind noise signal and the second designated wind noise signal are respectively obtained;

[0011] Determining the phases of the first designated wind noise signal and the second designated wind noise signal according to the autocorrelation function;

[0012] Analyze the voice signal obtained by the call microphone to determine the frequency and phase of the voice signal;

[0013] Performing Fourier transform on the speech signal, dividing the calculated speech signal into multiple signal segments and then comparing them with the first specified wind noise signal and the second specified wind noise signal respectively, marking the signal segments with high similarity to the first specified wind noise signal and the second specified wind noise signal as signal segments to be converted, and marking the remaining signal segments as signal segments not to be converted;

[0014] The signal segment to be converted is enhanced to obtain a target wind noise signal segment, the target wind noise signal segment and the unconverted signal segment are reorganized in the original order, and the reorganized speech signal is output.

[0015] As a preferred solution, determining the first power spectral density of the first wind noise signal and the second power spectral density of the second wind noise signal respectively according to the frequency domain of the first wind noise signal and the frequency domain of the second wind noise signal includes:

[0016] Determine the frame rates and frequencies of the first wind noise signal and the second wind noise signal respectively according to the frequency domain of the first wind noise signal and the frequency domain of the second wind noise signal;

[0017] According to the frame rates and frequencies of the first wind noise signal and the second wind noise signal, the first wind noise signal and the second wind noise signal are respectively divided into a plurality of overlapping signal segments;

[0018] Selecting a window function, and multiplying the window function by each signal segment to perform windowing processing on each signal segment;

[0019] Perform Fourier transform on the windowed signal segments to obtain the power spectrum density corresponding to each signal segment.

[0020] As a preferred solution, the calculation formula of the power spectrum density X(k) is as follows:

[0021]

[0022] Wherein, x[n] is the nth signal segment after windowing; N is the total number of signal segments; k is the frequency point, and the value range is usually 0 to N-1; j is the imaginary unit, satisfying j2=-1.

[0023] As a preferred solution, the signal enhancement of the signal segment to be converted to obtain the target wind noise signal segment includes:

[0024] Calculating a frequency difference and a phase difference between the first designated wind noise signal and the second designated wind noise signal to obtain a reference frequency difference and a reference phase difference;

[0025] Performing signal enhancement on the signal segment to be converted according to the reference frequency difference and the reference phase difference to obtain the target wind noise signal segment to be determined;

[0026] A similarity analysis is performed on the target wind noise signal segment to be determined and the second designated wind noise signal; if the similarity between the two is less than or equal to a preset similarity threshold, the target wind noise signal segment to be determined is used as the final target wind noise signal segment; if the similarity between the two is greater than the preset similarity threshold, the target wind noise signal segment to be determined is again signal enhanced until the enhanced target wind noise signal segment to be determined is compared with the second designated wind noise signal and the similarity between the two is less than or equal to the preset similarity threshold, then the enhanced target wind noise signal to be determined is used as the final target wind noise signal segment.

[0027] As a priority solution, the calculation formula of the target wind noise signal segment to be determined is as follows:

[0028] F(n)=f(n)·e j2πΔf

[0029] F(n)=f(n)·e j2πΔw

[0030] Among them, F(n) is the target wind noise signal segment to be determined, f(n) is the signal segment to be converted before enhancement, Δf is the reference frequency difference, Δw is the reference phase difference, j is the imaginary unit, and j satisfies 2 =-1.

[0031] As a preferred solution, an air guide groove is provided on the shell of the OWS ear clip headphone, and the auxiliary microphone corresponds to the air guide groove.

[0032] As a preferred solution, the feedforward microphone is installed on a side of the housing of the OWS ear clip headphone away from the ear.

[0033] As a preferred solution, the call microphone is installed on the side of the shell of the OWS ear clip headset close to the mouth.

[0034] A computer-readable storage medium stores a plurality of instructions, wherein the instructions are suitable for being loaded by a processor and executing the steps of the above-mentioned OWS ear-clip headphone anti-wind noise method.

[0035] An electronic device comprises a memory, a processor and program instructions stored in the memory and executable on the processor, wherein the program instructions, when executed by the processor, implement the above-mentioned method for suppressing wind noise of OWS ear clip headphones.

[0036] Compared with the prior art, the present invention has obvious advantages and beneficial effects. Specifically, it can be seen from the above technical solution that:

[0037] The anti-wind noise method of the OWS ear-clip earphone of the present application first performs Fourier transform conversion on the acquired first wind noise signal and the second wind noise signal to obtain the frequency domain of the first wind noise signal and the frequency domain of the second wind noise signal; according to the frequency domain of the first wind noise signal and the frequency domain of the second wind noise signal, a first power spectral density of the first wind noise signal and a second power spectral density of the second wind noise signal are determined, and then the autocorrelation function of the first specified wind noise signal and the second specified wind noise signal is determined, and the phase characteristics of the first specified wind noise signal and the second specified wind noise signal are inferred by analyzing the shape and peak position of the autocorrelation function; then The voice signal is subjected to Fourier transformation, and the signal segments to be converted in the voice signal that have a high similarity to the wind noise signal are enhanced to obtain the target wind noise signal segments, and then the target wind noise signal segments are reorganized in the original order, and the reorganized voice signal is output; therefore, in the present application, the voice signal and the wind noise signal can be clearly distinguished, and the influence of wind noise on the use of the headset can be reduced, so that the user can hear the content of the voice call more clearly and accurately in the wind noise scene, and the headset can have higher clarity when making calls and listening to music, and the user experience is also better.

[0038] In order to more clearly illustrate the structural features and effects of the present invention, the present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 is a flow chart of a method for resisting wind noise of an OWS ear clip headphone in one embodiment of the present invention;

[0040] Figure 2 is a schematic structural diagram of an OWS ear-clip headphone according to an embodiment of the present invention;

[0041] Figure 3 is a schematic structural diagram of a feedforward microphone disposed on an OWS ear-clip headphone in one embodiment of the present invention;

[0042] Figure 4It is a schematic structural diagram of an auxiliary microphone disposed on an OWS ear clip headset in one embodiment of the present invention.

[0043] Description of the accompanying drawings: 101, OWS ear-clip headphones; 102, feedforward microphone; 103, air guide slot; 104, auxiliary microphone. DETAILED DESCRIPTION

[0044] The present invention is further described below in conjunction with the accompanying drawings and specific embodiments.

[0045] like Figure 1-Figure 4 As shown, this embodiment discloses an anti-wind noise method for an OWS ear clip headset, which is mainly used to perform anti-wind noise processing on a voice signal heard by a user when using the headset for a voice call.

[0046] The OWS ear clip headset 101 includes a feedforward microphone 102, an auxiliary microphone 104 and a call microphone, wherein the feedforward microphone 102 is used to directly obtain a wind noise signal, the auxiliary microphone 104 is used to collect a wind noise signal that has undergone physical noise reduction, and the call microphone is used to collect a voice signal during a call;

[0047] The anti-wind noise method comprises the following steps:

[0048] Step S100: performing Fourier transform on the first wind noise signal collected by the feedforward microphone 102 and the second wind noise signal collected by the auxiliary microphone 104 to obtain the frequency domain of the first wind noise signal and the frequency domain of the second wind noise signal;

[0049] Step S200: determining a first power spectral density of the first wind noise signal and a second power spectral density of the second wind noise signal respectively according to the frequency domain of the first wind noise signal and the frequency domain of the second wind noise signal;

[0050] Step S300: marking a signal segment in the first wind noise signal whose first power spectrum density is greater than a preset first threshold as a first designated wind noise signal, and marking a signal segment in the second wind noise signal whose second power spectrum density is less than a preset second threshold as a second designated wind noise signal; wherein the first wind noise signal is a directly acquired wind noise signal, and therefore, the intensity and frequency of the first designated wind noise signal are both relatively large, and a significant wind noise influence may be formed; and the second wind noise signal is a wind noise signal collected by the earphone after physical noise reduction, and therefore, the second designated wind noise signal is a wind noise signal that is difficult to eliminate by physical noise reduction, and its frequency is relatively low, and its stability is relatively good;

[0051] Step S400: Analyze the first designated wind noise signal and the second designated wind noise signal respectively. When the peak values ​​of the frequency domains of the first designated wind noise signal and the second designated wind noise signal are greater than a preset frequency domain threshold, use the corresponding peak points as the main frequency components and obtain the frequencies of the first designated wind noise signal and the second designated wind noise signal respectively.

[0052] Step S500: determining the phases of the first designated wind noise signal and the second designated wind noise signal according to the autocorrelation function;

[0053] Step S600: Analyze the voice signal acquired by the call microphone to determine the frequency and phase of the voice signal;

[0054] Step S700: Perform Fourier transform on the speech signal, and divide the calculated speech signal into multiple signal segments, and then compare them with the first specified wind noise signal and the second specified wind noise signal respectively, mark the signal segments with high similarity to the first specified wind noise signal and the second specified wind noise signal as signal segments to be converted, and mark the remaining signal segments as unconverted signal segments;

[0055] Step S800: performing signal enhancement on the signal segment to be converted to obtain a target wind noise signal segment, recombining the target wind noise signal segment and the unconverted signal segment in the original order, and outputting the recombined speech signal.

[0056] In the present application, through the processing of the above method, when the user uses headphones to make a voice call in a wind noise scene (for example: the wind is strong outdoors, or the user is riding a bicycle, running, etc. at the same time), the frequency and phase characteristics of the wind noise signal obtained by the feedforward microphone and the auxiliary microphone can be used to identify the signal segment in the voice signal that has a high similarity with the frequency and phase characteristics of the wind noise signal, and then the corresponding signal segment is enhanced, so as to convert the received voice signal into a call voice signal with a low similarity with the spectral characteristics of the wind noise signal for output, so that there is a clear distinction between the voice signal and the wind noise signal, reducing the impact of wind noise on the use of the headphones, and facilitating the user to hear the content of the voice call more clearly and accurately in wind noise scenes, so that the clarity of the headphones when talking and listening to music is higher, and the user experience is also better.

[0057] In this embodiment, in step S200, determining a first power spectral density of the first wind noise signal and a second power spectral density of the second wind noise signal respectively according to the frequency domain of the first wind noise signal and the frequency domain of the second wind noise signal includes:

[0058] Step 201: determining the frame rates and frequencies of the first wind noise signal and the second wind noise signal respectively according to the frequency domain of the first wind noise signal and the frequency domain of the second wind noise signal;

[0059] Step 202: dividing the first wind noise signal and the second wind noise signal into a plurality of overlapping signal segments according to the frame rates and frequencies of the first wind noise signal and the second wind noise signal;

[0060] Step 203: Select a window function, and multiply the window function by each signal segment to perform windowing processing on each signal segment; wherein, the windowing processing may use a Hanning window, a Hamming window, a Blackman window, etc.

[0061] Step 204: Perform Fourier transform on the windowed signal segments to obtain the power spectrum density corresponding to each signal segment.

[0062] Furthermore, the power spectral density is the power distribution of the signal at different frequencies, which can be obtained by discrete Fourier transform, where the calculation formula of the power spectral density X(k) is as follows:

[0063]

[0064] Wherein, x[n] is the nth signal segment after windowing; N is the total number of signal segments; k is the frequency point, and the value range is usually 0 to N-1; j is the imaginary unit, satisfying j2=-1.

[0065] In this embodiment, before step S600, it is first determined whether the user is in a call state. Here, whether the user is in a call state can be determined in combination with the application state of electronic devices such as mobile phones (for example, the voice call state of the mobile phone is changed to the input state), so as to determine whether the user is making a voice call. After determination, the call microphone is started to obtain the user's voice signal through the call microphone.

[0066] In this embodiment, in step S600, the frequency and phase of the received voice signal can be determined in the following manner.

[0067] First, how to determine the frequency: Bluetooth headsets receive digital audio signals sent from mobile devices (such as mobile phones, electronic watches, etc.). The audio source specifies the sampling frequency of the audio when encoding the audio data. After receiving these signals, the Bluetooth headset can obtain the basic frequency parameters of the audio (the Bluetooth headset itself has a certain frequency response range specification when it is designed. For example, the common Bluetooth headset frequency response range may be 20Hz-20kHz. In most cases, the Bluetooth headset outputs sound according to this designed frequency range. When the audio source is played normally, it will try to accurately restore the sound within this frequency range). At the same time, the DSP chip inside the Bluetooth headset processes the received digital audio signal. It can adjust and optimize the audio frequency according to the preset algorithms and parameters (for example, in order to enhance the bass effect, the DSP chip may amplify the audio signal in the low-frequency part). Therefore, the digital audio signal is converted into an analog audio signal output by the DAC. The DAC can more accurately restore the various frequency components in the digital audio signal, so that the frequency of the voice signal can be directly known. Moreover, the output audio frequency is usually consistent with the input digital audio signal, that is, the frequency of the received voice signal is usually consistent with the frequency of the digital audio signal transmitted by the mobile device.

[0068] Secondly, how to determine the phase: During the encoding and transmission process of the audio source, the phase information of the received voice signal will be included in the digital audio data. Therefore, when the Bluetooth headset receives this data, it also receives the phase information. For example, in the Bluetooth audio transmission protocol, the phase integrity of the audio signal will be guaranteed as much as possible so that the Bluetooth headset can accurately restore the phase of the audio, that is, the phase of the received voice signal is usually consistent with the phase of the digital audio signal transmitted by the mobile device.

[0069] In this embodiment, in step S800, the signal enhancement of the signal segment to be converted to obtain the target wind noise signal segment includes:

[0070] Step S801: Calculate the frequency difference and phase difference between the first designated wind noise signal and the second designated wind noise signal to obtain a reference frequency difference and a reference phase difference;

[0071] Step S802: performing signal enhancement on the signal segment to be converted according to the reference frequency difference and the reference phase difference to obtain a target wind noise signal segment to be determined;

[0072] Step S803: performing similarity analysis on the target wind noise signal segment to be determined and the second designated wind noise signal; if the similarity between the two is less than or equal to a preset similarity threshold, the target wind noise signal segment to be determined is used as the final target wind noise signal segment; if the similarity between the two is greater than the preset similarity threshold, signal enhancement is performed again on the target wind noise signal segment to be determined, until the enhanced target wind noise signal segment to be determined is compared with the second designated wind noise signal and the similarity between the two is less than or equal to the preset similarity threshold, then the enhanced target wind noise signal to be determined is used as the final target wind noise signal segment.

[0073] Therefore, after the processing from step S801 to step S803, the target wind noise signal segment after signal enhancement can be obtained.

[0074] It is worth noting that in step S803, the target wind noise signal segment to be determined is analyzed for similarity with the second designated wind noise signal. The main reason is that: the intensity and frequency of the first designated wind noise signal are both large, which can form a significant wind noise impact; the second designated wind noise signal is a wind noise signal that is difficult to eliminate by physical noise reduction, and its frequency is low and its stability is good. For the use scenario of this application, the user needs to make a voice call in a wind noise scene. Although the intensity and frequency of the first designated wind noise signal are both large, it can basically be filtered out after the physical noise reduction of the wind guide groove. Even if it is not completely filtered out, the remaining first designated wind noise signal is difficult to form a continuous wind noise signal segment, which has little effect on the user's ability to hear the content of the call. The second designated wind noise signal has better stability and is difficult to filter out by physical noise reduction. Conventional noise reduction methods filter or suppress it through algorithms, which easily leads to the loss of some details of the voice content, affecting the user's acquisition of complete voice information. Therefore, the present application enhances the target wind noise signal that is similar to the second designated wind noise signal, so that there is a large difference between the target wind noise signal and the second designated wind noise signal, so that the user can clearly hear the voice information in the target wind noise signal segment while retaining the second designated wind noise signal.

[0075] Furthermore, the calculation formula of the target wind noise signal segment to be determined is as follows:

[0076] F(n)=f(n)·e j2πΔf

[0077] F(n)=f(n)·e j2πΔw

[0078] Among them, F(n) is the target wind noise signal segment to be determined, f(n) is the signal segment to be converted before enhancement, Δf is the reference frequency difference, Δw is the reference phase difference, j is the imaginary unit, and j satisfies 2 =-1.

[0079] In the present application, after the signal enhancement processing in step S800, the target wind noise signal segment is now obviously different from the first designated wind noise signal and the second designated wind noise signal. After the target wind noise signal segment is reorganized into a voice signal, it can be ensured that when the user is talking, the answering voice signal output by the Bluetooth headset can be distinguished from the wind noise, thereby making the answering voice signal obviously different from the wind noise signal, which is convenient for the user to hear the content of the voice call more accurately in a windy noise scene.

[0080] In addition, the present application mainly performs wind noise reduction processing on the voice signal heard by the user when using headphones for a call. Similarly, due to the influence of wind noise, the call microphone also has a certain degree of blur when receiving the call voice signal input by the user when speaking. Therefore, the call voice signal is processed in the same way according to the wind noise reduction method of the present application, so that the call voice signal sent by the Bluetooth headset (that is, the OWS ear clip headset of the present application) to the mobile device can also be processed to reduce wind noise, thereby ensuring that the user on the other end of the call can also hear the content of the voice call more accurately.

[0081] See also Figure 2-Figure 4 The feedforward microphone 102 is installed on the side of the shell of the OWS ear clip headphone 101 away from the ear, wherein a part of the structure of the feedforward microphone 102 is exposed outside the earphone shell, so that the external wind noise signal can be directly obtained, and the intensity and frequency of the wind noise signal are relatively large.

[0082] See also Figure 2-Figure 4 The OWS ear clip earphone has an air guide groove on its housing, which is located on the arc surface on the side of the earphone housing, and the auxiliary microphone corresponds to the air guide groove. The auxiliary microphone can be installed in the air guide groove, or installed in the earphone housing and corresponding to the air guide groove (see Figure 4 ); specifically, the air guide groove is on the curved wall surface of the earphone shell, which can make the wind quickly disperse along the curved wall surface around the air guide groove, reduce the energy of the wind blowing into the air guide groove, and thus reduce the wind noise, so as to achieve the effect of preventing wind noise; at the same time, the design of the air guide groove can reduce the wind noise entering the earphone shell, and thus reduce the auxiliary microphone from obtaining the wind noise signal; therefore, the wind noise signal obtained by the auxiliary microphone is the second wind noise signal after the physical noise reduction processing. Therefore, the second designated wind noise signal is a wind noise signal that is difficult to eliminate by physical noise reduction, and its frequency is lower and the stability is better.

[0083] Furthermore, the call microphone (not shown in the figure) is installed on the side of the shell of the OWS ear clip headset 101 close to the mouth, so as to conveniently pick up the user's voice signal.

[0084] The design focus of the anti-wind noise method of the OWS ear-clip earphone provided by the present invention is: the anti-wind noise method of the OWS ear-clip earphone of the present application first performs Fourier transform conversion on the acquired first wind noise signal and the second wind noise signal to obtain the frequency domain of the first wind noise signal and the frequency domain of the second wind noise signal; according to the frequency domain of the first wind noise signal and the frequency domain of the second wind noise signal, a first power spectral density of the first wind noise signal and a second power spectral density of the second wind noise signal are determined, and then the autocorrelation function of the first specified wind noise signal and the second specified wind noise signal is determined, and the first specified wind noise signal and the second specified wind noise signal are inferred by analyzing the shape and peak position of the autocorrelation function. 2. Specify the phase characteristics of the wind noise signal; then perform Fourier transform on the voice signal, and enhance the signal segments to be converted in the voice signal that have a high similarity to the wind noise signal to obtain the target wind noise signal segments, and then reorganize the target wind noise signal segments in the original order, and then output the reorganized voice signal; therefore, in the present application, there can be a clear distinction between the voice signal and the wind noise signal, reducing the impact of wind noise on the use of the headset, so that users can hear the content of the voice call more clearly and accurately in wind noise scenarios, and the headset can have higher clarity when talking on the phone and listening to music, and the user experience is also better.

[0085] It is worth noting that both the first wind noise signal and the second wind noise signal are continuous noise signals and have corresponding autocorrelation functions, and this content is prior art and will not be described in detail here.

[0086] The present application also provides a computer-readable storage medium storing a plurality of instructions, wherein the instructions are suitable for being loaded by a processor and executing the steps of the above-mentioned method for suppressing wind noise of the OWS ear clip-on headphones.

[0087] The present application also provides an electronic device, including a memory, a processor, and program instructions stored in the memory and executable on the processor, wherein the program instructions, when executed by the processor, implement the above-mentioned OWS ear-clip headphone wind noise reduction method.

[0088] In one embodiment, the processor may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chip. The processor is generally used to control the overall operation of the electronic device. In this embodiment, the processor is used to run the program code stored in the readable storage medium or process data.

[0089] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a readable storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for a terminal (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in each embodiment of the present invention.

[0090] The above description is only a preferred embodiment of the present invention and does not limit the technical scope of the present invention. Therefore, any slight modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention are still within the scope of the technical solution of the present invention.

Claims

1. A method for resisting wind noise of an OWS ear clip headset, the OWS ear clip headset comprising a feedforward microphone, an auxiliary microphone and a call microphone, the feedforward microphone is used to directly obtain a wind noise signal, the auxiliary microphone is used to collect a wind noise signal after physical noise reduction, and the call microphone is used to collect a voice signal during a call; characterized in that: The anti-wind noise method comprises the following steps: Performing Fourier transform on the first wind noise signal collected by the feedforward microphone and the second wind noise signal collected by the auxiliary microphone respectively to obtain the frequency domain of the first wind noise signal and the frequency domain of the second wind noise signal; Determine, according to the frequency domain of the first wind noise signal and the frequency domain of the second wind noise signal, a first power spectral density of the first wind noise signal and a second power spectral density of the second wind noise signal; Marking a signal segment in the first wind noise signal where the first power spectrum density is greater than a preset first threshold as a first designated wind noise signal, and marking a signal segment in the second wind noise signal where the second power spectrum density is less than a preset second threshold as a second designated wind noise signal; The first designated wind noise signal and the second designated wind noise signal are analyzed respectively, and when the peak values ​​of the frequency domains of the first designated wind noise signal and the second designated wind noise signal are greater than a preset frequency domain threshold, the corresponding peak points are respectively used as the main frequency components, and the frequencies of the first designated wind noise signal and the second designated wind noise signal are respectively obtained; Determining the phases of the first designated wind noise signal and the second designated wind noise signal according to the autocorrelation function; Analyze the voice signal obtained by the call microphone to determine the frequency and phase of the voice signal; Performing Fourier transform on the speech signal, dividing the calculated speech signal into multiple signal segments and then comparing them with the first specified wind noise signal and the second specified wind noise signal respectively, marking the signal segments with high similarity to the first specified wind noise signal and the second specified wind noise signal as signal segments to be converted, and marking the remaining signal segments as signal segments not to be converted; The signal segment to be converted is enhanced to obtain a target wind noise signal segment, the target wind noise signal segment and the unconverted signal segment are reorganized in the original order, and the reorganized speech signal is output.

2. The method for suppressing wind noise of OWS ear clip headphones according to claim 1, characterized in that: The determining, according to the frequency domain of the first wind noise signal and the frequency domain of the second wind noise signal, respectively, a first power spectral density of the first wind noise signal and a second power spectral density of the second wind noise signal comprises: Determine the frame rates and frequencies of the first wind noise signal and the second wind noise signal respectively according to the frequency domain of the first wind noise signal and the frequency domain of the second wind noise signal; According to the frame rates and frequencies of the first wind noise signal and the second wind noise signal, the first wind noise signal and the second wind noise signal are respectively divided into a plurality of overlapping signal segments; Selecting a window function, and multiplying the window function by each signal segment to perform windowing processing on each signal segment; Perform Fourier transform on the windowed signal segments to obtain the power spectrum density corresponding to each signal segment.

3. The method for suppressing wind noise of OWS ear clip headphones according to claim 2, characterized in that: The calculation formula of the power spectrum density X(k) is as follows: Wherein, x[n] is the nth signal segment after windowing; N is the total number of signal segments; k is the frequency point, and the value range is usually 0 to N-1; j is the imaginary unit, satisfying j2=-1.

4. The method for suppressing wind noise of OWS ear clip headphones according to claim 1, characterized in that: The step of performing signal enhancement on the signal segment to be converted to obtain the target wind noise signal segment comprises: Calculating a frequency difference and a phase difference between the first designated wind noise signal and the second designated wind noise signal to obtain a reference frequency difference and a reference phase difference; Performing signal enhancement on the signal segment to be converted according to the reference frequency difference and the reference phase difference to obtain the target wind noise signal segment to be determined; A similarity analysis is performed on the target wind noise signal segment to be determined and the second designated wind noise signal; if the similarity between the two is less than or equal to a preset similarity threshold, the target wind noise signal segment to be determined is used as the final target wind noise signal segment; if the similarity between the two is greater than the preset similarity threshold, the target wind noise signal segment to be determined is again signal enhanced until the enhanced target wind noise signal segment to be determined is compared with the second designated wind noise signal and the similarity between the two is less than or equal to the preset similarity threshold, then the enhanced target wind noise signal to be determined is used as the final target wind noise signal segment.

5. The method for suppressing wind noise of OWS ear clip headphones according to claim 4, characterized in that: The calculation formula of the target wind noise signal segment to be determined is as follows: F(n)=f(n)·e j2πΔf F(n)=f(n)·e j2πΔw Among them, F(n) is the target wind noise signal segment to be determined, f(n) is the signal segment to be converted before enhancement, Δf is the reference frequency difference, Δw is the reference phase difference, j is the imaginary unit, and j satisfies 2 =-1.

6. The method for suppressing wind noise of OWS ear clip headphones according to claim 1, characterized in that: The shell of the OWS ear clip earphone is provided with an air guide groove, and the auxiliary microphone corresponds to the air guide groove.

7. The method for suppressing wind noise of OWS ear clip headphones according to claim 1, characterized in that: The feedforward microphone is installed at a side of the housing of the OWS ear clip headphone away from the ear.

8. The method for suppressing wind noise of OWS ear clip headphones according to claim 1, characterized in that: The communication microphone is installed on a side of the housing of the OWS ear clip type headset close to the mouth.

9. A computer-readable storage medium, characterized in that: A plurality of instructions are stored, and the instructions are suitable for being loaded by a processor and executing the steps of the anti-wind noise method of the OWS ear clip headphone according to any one of claims 1 to 8.

10. An electronic device, characterized in that: The invention comprises a memory, a processor and program instructions stored in the memory and executable on the processor, wherein when the program instructions are executed by the processor, the anti-wind noise method of the OWS ear clip type earphone as claimed in any one of claims 1 to 8 is implemented.

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