Self-adaptive noise reduction method and system for Bluetooth headset

Abstract

The invention provides an adaptive noise reduction method and system for a Bluetooth headset, and the method comprises the steps: obtaining a wearing deviation value of the Bluetooth headset through a speed change value and an angle change value of the Bluetooth headset; determining a frequency band sensitivity value corresponding to the wearing deviation value; calculating a gain value, a cut-off frequency value and a phase compensation value corresponding to the frequency band sensitivity value by adopting an adaptive filtering algorithm; and performing adaptive noise reduction on the Bluetooth earphone by using the gain value, the cut-off frequency value and the phase compensation value. According to the method and the device, the noise reduction parameters of the Bluetooth headset can be adjusted in real time according to the speed change and angle change conditions of wearing the Bluetooth headset, the reliability of the noise reduction effect is maintained under different wearing conditions, and the use experience of a user is improved.

Description

Technical Field

[0001] This application relates to the technical field of electronic products, and particularly to an adaptive noise reduction method and system for a Bluetooth headset. Background Art

[0002] The noise reduction technology of Bluetooth headsets plays a crucial role in the field of modern audio devices, directly affecting the immersive experience of users in complex acoustic environments. With the continuous improvement of consumers' demands for the sound quality and comfort of Bluetooth headsets, the noise reduction technology has become the core driving force for promoting innovation in the audio industry. However, the existing noise reduction solutions are implemented using fixed algorithm models, making it difficult to adapt to the diversity of users' wearing positions. Especially in dynamic scenarios, subtle changes in the wearing position of the headset can lead to a significant decline in the noise reduction effect, affecting the user experience. Summary of the Invention

[0003] In view of this, this application provides an adaptive noise reduction method for a Bluetooth headset, mainly aiming to adapt to the wearing habits and ear canal structures of different users, adaptively adjust the noise reduction performance, and better exert the noise reduction performance.

[0004] To achieve the above object, in the first aspect of this application, an adaptive noise reduction method for a Bluetooth headset is disclosed. The method includes: Obtaining the wearing offset value of the Bluetooth headset by using the speed change value and the angle change value of the Bluetooth headset; Determining the frequency band sensitivity value corresponding to the wearing offset value; Calculating the gain value, the cut-off frequency value, and the phase compensation value corresponding to the frequency band sensitivity value by using an adaptive filtering algorithm; Performing adaptive noise reduction on the Bluetooth headset by using the gain value, the cut-off frequency value, and the phase compensation value.

[0005] In the second aspect of this application, an embodiment provides an adaptive noise reduction system for a Bluetooth headset. The system includes: An obtaining module, configured to obtain the wearing offset value of the Bluetooth headset by using the speed change value and the angle change value of the Bluetooth headset; A determining module, configured to determine the frequency band sensitivity value corresponding to the wearing offset value; A calculating module, configured to calculate the gain value, the cut-off frequency value, and the phase compensation value corresponding to the frequency band sensitivity value by using an adaptive filtering algorithm; A noise reduction module, configured to perform adaptive noise reduction on the Bluetooth headset by using the gain value, the cut-off frequency value, and the phase compensation value.

[0006] In the third aspect of this application, an embodiment provides an electronic device, including: At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the method according to any one of the first aspects disclosed.

[0007] In the fourth aspect of the embodiments of the present application, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the method described in the first aspect is implemented.

[0008] In summary, according to the technical solutions disclosed in the present application, the present application discloses an adaptive noise reduction method for a Bluetooth headset, which uses the speed change value and angle change value of the Bluetooth headset to obtain the wearing offset value of the Bluetooth headset; determines the frequency band sensitivity value corresponding to the wearing offset value; adopts an adaptive filtering algorithm to calculate the gain value, cut-off frequency value, and phase compensation value corresponding to the frequency band sensitivity value; and uses the gain value, cut-off frequency value, and phase compensation value to perform adaptive noise reduction on the Bluetooth headset. The present application can adjust the noise reduction parameters of the Bluetooth headset in real time according to the speed change and angle change of wearing the Bluetooth headset, so as to maintain the reliability of the noise reduction effect under different wearing conditions and improve the user experience.

[0009] The above description is only an overview of the technical solutions of the present application. In order to be able to understand the technical means of the present application more clearly, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features, and advantages of the present application more obvious and understandable, the specific embodiments of the present application are specifically listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] The drawings here are incorporated into the specification and form a part of this specification, showing the embodiments in line with the present application, and are used together with the specification to explain the principles of the present application.

[0011] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required to be used in the description of the embodiments or the prior art. Obviously, for those of ordinary skill in the art, without creative efforts, other drawings can also be obtained according to these drawings.

[0012] Figure 1 Shows the flowchart of the adaptive noise reduction method for a Bluetooth headset provided by the embodiments of the present application; Figure 2 Shows the system structure diagram of the adaptive noise reduction method for a Bluetooth headset provided by the embodiments of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0013] In order to more clearly understand the above-mentioned objects, features, and advantages of the present application, the solution of the present application will be further described below. It should be noted that, without conflict, the embodiments of the present application and the features in the embodiments can be combined with each other.

[0014] To solve the problem that in a dynamic scenario, slight changes in the earphone wearing position will cause a significant decrease in the noise reduction effect, affecting the user experience. The present application provides the following embodiments to solve the above problems: This embodiment provides an adaptive noise reduction method for a Bluetooth earphone, as Figure 1 shown, which is a flowchart of the method of this embodiment. The method of this embodiment specifically may include the following steps: Step 101, using the speed change value and the angle change value of the Bluetooth earphone, obtain the wearing offset value of the Bluetooth earphone.

[0015] By means of the built-in acceleration sensor and gyroscope, obtain the real-time angle data of the earphone in three-dimensional space, and combine the sound field feedback signal collected by the ear canal microphone to determine the earphone wearing angle offset θ (unit: radian, range -0.5 to 0.5) and the initial reflection coefficient r of the ear canal sound field (range 0 to 1). Collect the real-time angle data of the earphone in three-dimensional space through the acceleration sensor and gyroscope, use the Kalman filter algorithm to fuse the angle data to obtain the first angle data. According to the first angle data, calibrate with the reference angle of the preset standard wearing position, calculate the rotation angle of the earphone relative to the standard position through Euler angle conversion, and determine the wearing angle offset θ. Collect the sound field feedback signal through the ear canal microphone, use the adaptive filter to denoise the sound field signal to obtain the first sound field signal. Extract the reflection characteristics from the first sound field signal, and use the linear regression algorithm to calculate the initial reflection coefficient r of the ear canal sound field to obtain the reflection coefficient estimation value.

[0016] Specifically, collecting the real-time angle data of the earphone in three-dimensional space through the acceleration sensor and gyroscope focuses on capturing the spatial attitude of the earphone.

[0017] Exemplarily, the acceleration sensor can detect the acceleration changes of the earphone on the X, Y, and Z axes, while the gyroscope measures the angular velocity.

[0018] For example, when the user's head rotates, the gyroscope records an angular velocity change of 30 degrees per second, and the acceleration sensor detects that the gravitational acceleration on the Z axis is about 9.8 m / s². This kind of data reflects the real-time tilt or rotation of the earphone and provides a basis for subsequent angle fusion.

[0019] It should be noted that the sensor data is vulnerable to noise interference, such as the vibration when the user walks, which may cause angle deviation. Using the Kalman filter algorithm to fuse the angle data aims to optimize the data accuracy.

[0020] Specifically, Kalman filtering generates more stable first angle data by combining acceleration sensor and gyroscope data through prediction and update steps.

[0021] For example, when the user turns their head slowly, the gyroscope may record an angular velocity of 5 degrees per second, but the acceleration sensor misjudges it as 6 degrees due to noise. Kalman filtering outputs first angle data close to the true value of 5.2 degrees by weighing the credibility of the two. This method effectively reduces the influence of noise and improves the reliability of the angle data, laying a foundation for subsequent calibration. According to the first angle data, calibration is performed using the reference angle of the preset standard wearing position, and the wearing angle offset θ is calculated.

[0022] In one possible implementation, the standard wearing position is defined as the headphones being horizontally aligned with the ear canal, and the reference angle is 0 degrees. Assuming the first angle data shows that the headphones deviate 10 degrees from the horizontal, through Euler angle conversion, it is calculated that the headphones rotate 10 degrees around the Y axis, and the offset θ is 10 degrees.

[0023] Preferably, this calibration can dynamically adjust the audio output direction, for example, by adjusting the sound field phase to compensate for the auditory perception deviation caused by the offset, thereby optimizing the user experience. The sound field feedback signal is collected through the ear canal microphone, aiming to analyze the acoustic environment inside the ear canal.

[0024] In one embodiment, the ear canal microphone records the echo signal of the user's speech or ambient sound, for example, detecting a reflected wave with a frequency of 500 Hz. An adaptive filter is used to denoise the sound field signal, filtering out background noise such as the low-frequency hum of the air conditioner to obtain a clear first sound field signal.

[0025] It can be understood that the adaptive filter will dynamically adjust the filtering parameters according to the noise characteristics, for example, attenuating 60 Hz noise by 90% while retaining the main sound field characteristics. This denoising process significantly improves the signal quality and provides reliable data for subsequent feature extraction. Reflection features are extracted from the first sound field signal, and a linear regression algorithm is used to calculate the initial reflection coefficient r of the ear canal sound field.

[0026] For example, the reflection features include the time delay and intensity of the sound wave reflected from the ear canal wall. Assuming the signal shows a delay of 0.1 milliseconds and an intensity attenuation of 50%, linear regression analysis yields a reflection coefficient r of approximately 0.45. This estimated value reflects the influence of the ear canal shape on the sound field and can be used to optimize the audio algorithm.

[0027] For example, the high-frequency gain is adjusted based on the r value to improve sound clarity.

[0028] It should be noted that the high-precision estimation of the reflection coefficient contributes to personalized sound effect design and enhances the immersive auditory experience.

[0029] For example, the earphone detects that the wearing offset θ is 8 degrees and the reflection coefficient r is 0.4. Based on this, the system adjusts the sound field phase and high-frequency gain, ultimately enabling the user to perceive a more natural stereo effect. This comprehensive solution can dynamically adapt to the ear canals and wearing habits of different users, improve the sound quality consistency, and extend the applicability of the earphone usage scenarios.

[0030] Step 102: Determine the band sensitivity value corresponding to the wearing offset value.

[0031] Obtain the sound wave signal in the ear canal where the Bluetooth earphone is worn; use the band decomposition algorithm to divide the sound wave signal in the preset band into multiple sub-bands; determine the degradation coefficients of the noise reduction performance of the multiple sub-bands; use the sensitivity analysis algorithm to calculate the sensitivity values of the multiple sub-bands affected by the wearing offset value based on the degradation coefficients.

[0032] Obtain the sound field reflection coefficient corresponding to the sound wave signal; use the sound field simulation algorithm to generate a geometric characteristic model of the sound field in the ear canal by integrating the wearing offset value and the sound field reflection coefficient; use the geometric characteristic model to obtain the ear canal depth of the Bluetooth earphone and the updated reflection coefficient; if any value of the ear canal depth and / or the updated reflection coefficient is greater than the corresponding second preset threshold, use the band decomposition algorithm to divide the sound wave signal in the preset band into multiple sub-bands.

[0033] According to the earphone wearing angle and the angle offset θ (θ represents the angular deviation of the earphone relative to the ear canal), use the sound field simulation algorithm to calculate the sound field distribution and obtain the initial sound field characteristics. If the initial sound field characteristics do not match the preset sound field distribution threshold, adjust the reflection coefficient r (r represents the sound field reflection coefficient) through data iterative calculation to obtain the updated sound field reflection coefficient. Generate a geometric characteristic model based on the updated sound field reflection coefficient and the ear canal geometric parameters, and determine the ear canal depth d (d represents the ear canal depth, in millimeters). Use algorithm iterative optimization to obtain real-time updated values from the ear canal depth and the sound field characteristics to obtain the final sound field reflection coefficient.

[0034] Exemplarily, in the earphone sound field simulation, the calculation of the sound field distribution is based on the wearing angle and the angle offset θ. The sound field simulation algorithm analyzes the influence of the angle offset on the sound field by simulating the propagation of sound waves in the ear canal. Assuming θ is 0.3 radians, the algorithm can generate initial sound field characteristics, such as a sound pressure distribution diagram, through the sound wave propagation model in combination with the ear canal geometry. The core of this method is to convert the angle data into sound field parameters, which is applicable to the ear canal shapes of different users.

[0035] In a possible implementation, if the initial sound field characteristics do not match the preset threshold, the reflection coefficient r needs to be adjusted. The preset threshold is usually based on a standard ear canal model. For example, the peak sound pressure should be within a specified range. Suppose the initial r is 0.6, but the sound field distribution shows that the echo is too strong. Through iterative calculation, the algorithm gradually adjusts r to 0.4, and re - simulates the sound field until it matches the threshold. This iterative method can adapt to the differences in users' wearing habits.

[0036] Specifically, the update of the reflection coefficient r combines the ear canal geometric parameters to generate a geometric characteristic model. The ear canal geometric parameters include the ear canal length and curvature. The algorithm calculates the ear canal depth d through the sound field reflection data.

[0037] For example, based on the updated r of 0.4 and combined with the ear canal curvature analysis, the algorithm estimates d to be 12 millimeters. The geometric characteristic model can further refine the sound field distribution and optimize the audio output.

[0038] Preferably, the determination of the ear canal depth depends on the joint analysis of the sound field characteristics and geometric parameters.

[0039] For example, by simulating the reflection patterns of sound waves in ear canals of different depths, the algorithm can identify the impact of the difference in d of 10 millimeters or 15 millimeters on the sound field. A deeper ear canal may cause low - frequency enhancement, while a shallower ear canal may improve high - frequency clarity. This analysis helps to generate a personalized sound field model.

[0040] In one embodiment, the algorithm iteratively optimizes to obtain real - time updated values from the ear canal depth and sound field characteristics. Suppose the user wears the earphones during exercise and the angular offset θ changes dynamically. The algorithm updates r and d by collecting sound field data in real - time.

[0041] For example, when θ changes from 0.2 radians to 0.4 radians, the algorithm recalculates r to be 0.5 and d to be 13 millimeters to ensure the stability of the sound field. This real - time property is applicable to dynamic scenarios. It can be understood that the acquisition of the final sound field reflection coefficient depends on multiple rounds of iteration.

[0042] For example, in a quiet environment, the reflection coefficient r may stabilize at 0.45, while in a noisy environment, the algorithm may adjust to 0.5 to enhance the anti - noise ability. Through multi - scenario verification, the sound field characteristics can more accurately match the user's needs and enhance the immersion of the audio experience.

[0043] For example, for users with different ear canal depths, the algorithm can generate customized sound field models. Suppose the d of user A is 11 millimeters and the d of user B is 14 millimeters. The algorithm adjusts r to 0.42 and 0.48 respectively to generate different sound pressure distributions. This personalized solution can significantly improve the sound quality balance.

[0044] It should be noted that the core of sound field simulation and iterative optimization lies in data integration. The joint analysis of angular offset, reflection coefficient, and ear canal depth forms a closed-loop system. This method ensures a high degree of matching between the sound field characteristics and the actual wearing state through multi-dimensional data verification, providing users with a consistent audio experience.

[0045] If the deviations of the ear canal depth d (where d represents the ear canal depth) and the real-time sound field reflection coefficient r (where r represents the sound field reflection coefficient) from the preset acoustic model exceed the threshold T1, the frequency band decomposition algorithm is adopted to divide the mid-low frequency band into multiple sub-bands, obtaining a set of sub-band frequencies. According to the set of sub-band frequencies, the Fourier transform algorithm is used to extract the sound pressure amplitude and phase information of each sub-band, and to determine the sound field distribution characteristics of each sub-band. For the sound field distribution characteristics, the linear regression algorithm is used to calculate the noise reduction performance degradation coefficient k (where k represents the degree of noise reduction performance degradation) of each sub-band, obtaining a set of performance degradation coefficients.

[0046] Exemplarily, the core of the frequency band decomposition algorithm is to divide the mid-low frequency band into multiple sub-bands to analyze the sound field characteristics. The frequency band decomposition is based on the frequency characteristics of sound waves, decomposing the complex sound field into manageable subsets.

[0047] For example, assuming the mid-low frequency band ranges from 20 Hz to 1000 Hz, the algorithm divides it into five sub-bands, such as 20 - 200 Hz, 200 - 400 Hz, etc., and each sub-band corresponds to a specific set of frequencies. This decomposition method facilitates the refined analysis of the sound field performance at different frequencies, providing a data basis for subsequent processing.

[0048] In a possible implementation, the Fourier transform algorithm is used to extract the sound pressure amplitude and phase information of the sub-bands. The Fourier transform converts the time-domain signal into the frequency domain, revealing the distribution law of the sound pressure in each sub-band.

[0049] For example, for the 20 - 200 Hz sub-band, the algorithm analysis of the sound pressure amplitude may show a peak of 80 dB and a phase shift of 0.2 radians. These information reflect the propagation characteristics of the sound field in the ear canal. For example, the low frequency band may be enhanced due to the relatively deep ear canal depth. The extracted sound pressure and phase data provide a quantitative basis for the sound field distribution characteristics.

[0050] Specifically, the determination of the sound field distribution characteristics depends on the analysis of the sound pressure and phase of the sub-bands. The algorithm identifies the deviations by comparing the sound pressure distribution of each sub-band with the standard model.

[0051] For example, assuming that the sound pressure peak of the 200 - 400 Hz sub-band is too high and exceeds the preset threshold T1, it indicates that the sound field distribution of this frequency band may be affected by the ear canal geometry or the reflection coefficient. The algorithm generates a sound field distribution characteristic diagram, marking the deviation area, providing a reference for subsequent optimization.

[0052] Preferably, a linear regression algorithm is used to calculate the noise reduction performance degradation coefficient k. Linear regression quantifies the performance changes in each sub-band by analyzing the relationship between the sound field distribution characteristics and the noise reduction effect.

[0053] For example, the algorithm may find that the k value for the 400 - 600 Hz sub-band is 0.3, indicating a 30% degradation in noise reduction performance. This coefficient is obtained through regression analysis of the sound pressure amplitude and phase data, reflecting the impact of ear canal depth or reflection coefficient deviation on noise reduction. The set of performance degradation coefficients provides a basis for targeted optimization.

[0054] In one embodiment, assuming the ear canal depth d is 12 mm and the reflection coefficient r is 0.5, the algorithm detects that the sound field deviation in the mid - low frequency band exceeds the threshold T1. After frequency band decomposition, the sound pressure amplitude in the 20 - 200 Hz sub-band is on the high side, and the k value is 0.4. The algorithm optimizes the noise reduction effect by adjusting the sound field parameters in this sub-band, such as reducing the reflection coefficient to 0.45. This method ensures the matching of the sound field characteristics with the user's ear canal through multi-dimensional data analysis.

[0055] It can be understood that the advantage of the above method lies in the hierarchical processing of complex sound field data. Frequency band decomposition reduces the analysis difficulty, Fourier transform provides accurate frequency domain information, and linear regression quantifies the performance impact.

[0056] For example, for the 600 - 800 Hz sub-band, the algorithm may find that the k value is 0.2, and by fine-tuning the sound field parameters, the noise reduction stability can be significantly improved. This hierarchical analysis forms a closed-loop optimization mechanism.

[0057] It should be noted that the deviation analysis of the ear canal depth and reflection coefficient is the core. Through the sound field distribution characteristics of each sub-band, the algorithm can identify the source of the deviation.

[0058] For example, a deeper ear canal may cause an increase in the sound pressure in the low frequency band, and linear regression can quantify its negative impact on noise reduction. After targeted adjustment, the sound field distribution is more in line with expectations. This method ensures the reliability of the analysis results through multi-faceted data verification.

[0059] According to the noise reduction performance degradation coefficient k of each sub - frequency band, a sensitivity analysis algorithm is used to calculate the influence of the angular offset θ on the sub - frequency band, generating a frequency - band sensitivity matrix M, where M(i, j) represents the rate of change of the noise reduction performance degradation coefficient k of sub - frequency band i with respect to the angular offset θ, and the frequency - band sensitivity matrix is obtained. If there are element values in the frequency - band sensitivity matrix M that exceed a preset threshold T2, for the corresponding sub - frequency band, a fast Fourier transform algorithm is used to extract the sound pressure amplitude and phase information of the sub - frequency band, and the sound field distribution characteristics of the sub - frequency band are determined. According to the sound field distribution characteristics, a linear interpolation algorithm is used to calculate the change in sound pressure amplitude of the sub - frequency band at different angular offsets θ, generating a set of sound pressure amplitude changes, and the set of sound pressure amplitude changes is obtained. By normalizing the set of sound pressure amplitude changes, using the min - max normalization algorithm, the sound pressure amplitude change values are mapped to a preset range, and the normalized sensitivity values are judged.

[0060] Exemplarily, the sensitivity analysis algorithm is used to evaluate the influence of the angular offset θ on the noise reduction performance degradation coefficient k of the sub - frequency band. By analyzing the perturbation of the angular offset on the sound pressure distribution in the sound field, the algorithm generates the frequency - band sensitivity matrix M. The element M(i, j) of the matrix M represents the rate of change of the k value of sub - frequency band i with respect to the angular offset θ.

[0061] For example, assume that the rate of change of the k value of the sub - frequency band 20 - 200Hz at θ = 5° is 0.02, and M(1, 1)=0.02. This matrix form facilitates quantifying the influence of the angular offset on each frequency band and provides a basis for subsequent optimization.

[0062] In a possible implementation manner, the fast Fourier transform algorithm extracts the sound pressure amplitude and phase information of the sub - frequency band. The fast Fourier transform converts the time - domain sound field signal into the frequency - domain, revealing the sound pressure characteristics.

[0063] For example, for the sub - frequency band 200 - 400Hz, the algorithm may detect that the sound pressure amplitude is 75dB and the phase offset is 0.15 radians. These data reflect the sound field changes caused by the angular offset and lay a foundation for determining the sound field distribution characteristics.

[0064] Specifically, the linear interpolation algorithm is used to calculate the change in sound pressure amplitude of the sub - frequency band at different angular offsets θ. Assume that the sound pressure amplitudes of the sub - frequency band 400 - 600Hz at θ = 0°, 5°, 10° are 70dB, 72dB, 73dB respectively. The linear interpolation algorithm generates a continuous set of sound pressure amplitude changes by fitting these points. This set intuitively shows the dynamic change of the sound field with the angular offset and facilitates analyzing the sound field stability.

[0065] Preferably, the min - max normalization algorithm maps the set of sound pressure amplitude changes to a preset range from 0 to 1.

[0066] For example, the sound pressure amplitude varies in the range of 70 - 75 dB. After normalization, 70 dB is mapped to 0 and 75 dB is mapped to 1. The normalized sensitivity values facilitate the comparison of the responses of each sub - frequency band to the angular offset.

[0067] For example, the normalized sensitivity value of the 600 - 800 Hz sub - frequency band is 0.8, indicating that it is highly sensitive to angular offset.

[0068] In one embodiment, assume that in the frequency - band sensitivity matrix M, M(1,2) = 0.03 for the 20 - 200 Hz sub - frequency band at θ = 10°, which exceeds the threshold T2 = 0.025. The algorithm further extracts the sound pressure amplitude of this sub - frequency band as 78 dB and the phase offset as 0.1 radian, and calculates the sound pressure amplitude at θ = 15° as 79 dB through linear interpolation. After normalization, the sensitivity value is 0.9, indicating that the angular parameters need to be optimized for this frequency band.

[0069] It can be understood that the above - mentioned method improves the sound - field adaptability through multi - dimensional analysis. The sensitivity matrix quantifies the influence of angular offset, the fast Fourier transform provides accurate frequency - domain data, and linear interpolation and normalization enhance the data comparability.

[0070] For example, for the 800 - 1000 Hz sub - frequency band, the normalized sensitivity value is 0.3, indicating that its response to angular offset is low, and other frequency bands can be preferentially optimized. This hierarchical analysis ensures the pertinence and efficiency of sound - field adjustment.

[0071] It should be noted that the determination of the sound - field distribution characteristics depends on the sound pressure and phase data of the sub - frequency bands. For example, the set of sound - pressure amplitude changes of the 200 - 400 Hz sub - frequency band shows that when θ increases from 5° to 10°, the sound pressure only increases by 1 dB, indicating that this frequency band has strong robustness to angular offset. This characteristic analysis provides a reliable basis for accurately adjusting the sound - field parameters.

[0072] Step 103: Use an adaptive filtering algorithm to calculate the gain value, cut - off frequency value, and phase compensation value corresponding to the frequency - band sensitivity value.

[0073] Obtain the normalized sensitivity values from the frequency band sensitivity matrix M. Using the linear interpolation algorithm, calculate the sensitivity change curves of each sub - frequency band at different angular offsets θ, and obtain the first sensitivity change set. According to the first sensitivity change set, use the adaptive filtering algorithm to dynamically calculate the feedback gain G and the filter cut - off frequency f, and generate the first noise reduction parameter set. If the feedback gain G or the cut - off frequency f in the first noise reduction parameter set exceeds the preset threshold, then use the fast Fourier transform algorithm to extract the phase information of the corresponding sub - frequency band, calculate the phase compensation factor φ, and obtain the second noise reduction parameter set. By normalizing the second noise reduction parameter set, using the min - max normalization algorithm, map the feedback gain G, the cut - off frequency f, and the phase compensation factor φ to the preset range, and determine the real - time optimized noise reduction parameter set.

[0074] Exemplarily, the process of obtaining the normalized sensitivity values from the frequency band sensitivity matrix M aims to quantify the response characteristics of each sub - frequency band to angular offset. The elements of the sensitivity matrix M reflect the change rate of the sub - frequency band noise reduction performance degradation coefficient k with respect to the angular offset θ. The normalization process maps the sensitivity values to the range of 0 to 1 through the min - max normalization algorithm, facilitating the comparison of the response differences of different sub - frequency bands.

[0075] For example, the normalized sensitivity value of the 20 - 200Hz sub - frequency band is 0.7, while that of the 400 - 600Hz sub - frequency band is 0.4, indicating that the former is more sensitive to angular offset. This method provides a standardized data basis for subsequent parameter optimization by unifying the dimension.

[0076] In a possible implementation, the linear interpolation algorithm is used to generate the sensitivity change curves of each sub - frequency band. Assume that the normalized sensitivity values of the 200 - 400Hz sub - frequency band at θ = 0°, 5°, 10° are 0.3, 0.5, 0.6 respectively. The linear interpolation fits these points to generate a continuous sensitivity change curve, forming the first sensitivity change set. Such a curve intuitively shows the dynamic trend of sensitivity with angular offset.

[0077] For example, the curve shows that the sensitivity change of the 600 - 800Hz sub - frequency band is gentle between θ = 5° and 10°, indicating that its response to angular offset is relatively stable.

[0078] Specifically, the adaptive filtering algorithm dynamically calculates the feedback gain G and the filter cut - off frequency f according to the first sensitivity change set. The algorithm analyzes the sensitivity change trend and adjusts the filter parameters to optimize the noise reduction effect.

[0079] For example, for the 20 - 200 Hz sub - band, the sensitivity changes drastically. The algorithm may set a relatively high feedback gain G = 12 dB and a relatively low cut - off frequency f_c = 150 Hz to enhance the low - frequency noise reduction ability. For the 800 - 1000 Hz sub - band, the sensitivity changes less, so G may be set to 6 dB and f to 900 Hz to balance the high - frequency response. This dynamic adjustment ensures the pertinence of the parameter set.

[0080] It should be noted that if G or f_c in the first noise reduction parameter set exceeds the preset threshold, for example, G > 15 dB or f_c > 1000 Hz, the fast Fourier transform algorithm will extract the phase information of the corresponding sub - band. Assuming that the phase offset of the 400 - 600 Hz sub - band is 0.2 radians, the algorithm calculates the phase compensation factor φ = 0.15 radians and generates the second noise reduction parameter set. Phase compensation improves the noise reduction accuracy by correcting the phase deviation of the sound field.

[0081] For example, after compensation, the sound pressure distribution in this sub - band is more uniform and the noise reduction effect is more stable.

[0082] Preferably, the min - max normalization algorithm processes the second noise reduction parameter set, mapping G, f_c, and φ to the range of 0 to 1.

[0083] For example, the range of the feedback gain G is 6 - 12 dB. After normalization, 6 dB is mapped to 0 and 12 dB is mapped to 1; the range of the cut - off frequency f is 150 - 900 Hz. After normalization, 150 Hz is 0 and 900 Hz is 1. This normalization facilitates parameter comparison and real - time optimization.

[0084] For example, after normalization, for the 20 - 200 Hz sub - band, G = 0.8, f_c = 0.2, φ = 0.5, clearly reflecting the optimization focus.

[0085] In one embodiment, the real - time optimized noise reduction parameter set is determined by comprehensively analyzing the normalized parameters.

[0086] For example, the normalized parameters of the 600 - 800 Hz sub - band show G = 0.4, f_c = 0.7, φ = 0.3, indicating that the response of this frequency band to the angle offset is moderate. During optimization, G can be appropriately reduced to save computing resources, while keeping f_c stable to ensure the noise reduction effect. Such a parameter set provides an accurate basis for sound field adjustment.

[0087] It can be understood that the advantage of the above - mentioned method lies in generating optimized noise reduction parameters through multi - level analysis. Linear interpolation provides continuous sensitivity change data, adaptive filtering dynamically adjusts parameters, fast Fourier transform and phase compensation improve the accuracy, and normalization processing enhances the comparability of parameters.

[0088] For example, for the 200 - 400 Hz sub - band, the optimized parameter set significantly improves the sound field stability and has better noise reduction performance.

[0089] Determine the current parameter set corresponding to the Bluetooth headset before the speed change value and the angle change value appear. The current parameter set includes the current gain value, the current cut - off frequency value, and the current phase compensation value; determine the Euclidean distance between the current parameter set and the noise reduction parameter set. The noise reduction parameter set includes the gain value, the cut - off frequency value, and the phase compensation value; if the Euclidean distance is greater than the third threshold, use the exponential moving average algorithm to smooth the gain value and the phase compensation value to obtain the smoothed gain value and the smoothed phase compensation value; use the smoothed gain value, the cut - off frequency value, and the smoothed phase compensation value to perform adaptive noise reduction on the Bluetooth headset.

[0090] If the Euclidean distance between the real - time optimized noise reduction parameter set and the current parameter set exceeds the threshold T2 (T2 = 0.1), then through the exponential moving average algorithm, adjust the feedback gain G and the phase compensation factor φ to obtain the optimized parameter set after smoothing processing. Obtain the real - time noise reduction parameter set from the input signal, calculate the Euclidean distance between the real - time noise reduction parameter set and the current parameter set, and determine whether the Euclidean distance exceeds the preset threshold T2 to obtain the judgment result for triggering adjustment. If the judgment result for triggering adjustment is true, then use the exponential moving average algorithm to smooth the feedback gain G and the phase compensation factor φ to obtain the smoothed optimized parameter set. According to the smoothed optimized parameter set, dynamically adjust the gain value and the phase value of the signal processing module to determine the adjusted signal output parameters.

[0091] Exemplarily, when obtaining the real - time noise reduction parameter set from the input signal, the characteristic information of the signal can be obtained through frequency - domain analysis.

[0092] Specifically, the signal processing module performs a short - time Fourier transform on the input audio signal, extracts the amplitude and phase information of each frequency band, and forms the real - time noise reduction parameter set.

[0093] For example, in a moving car, the voice signal captured by the microphone is mixed with engine noise and wind noise. The system identifies that the low - frequency noise below 500 Hz is dominant through frequency - domain analysis and generates a parameter set including gain, cut - off frequency, and phase factor. This method can quickly respond to signal changes and ensure that the parameter set reflects the current noise characteristics.

[0094] In a possible implementation, when calculating the Euclidean distance between the real - time noise reduction parameter set and the current parameter set, the parameter set can be regarded as a multi - dimensional vector.

[0095] For example, the real-time parameter set includes a feedback gain G1 of -5 dB, a cut-off frequency f_c1 of 300 Hz, and a phase compensation factor φ1 of 0.2, while the current parameter set is G2 of -4 dB, f_c2 of 350 Hz, and φ2 of 0.3. The Euclidean distance determines whether the parameter change is significant by comparing the differences of these parameters.

[0096] Preferably, the preset threshold T2 can be set to 1.5. When the distance exceeds 1.5, it indicates that the noise environment has changed significantly, such as the noise spectrum shifting due to a car accelerating, triggering parameter adjustment. This judgment mechanism can effectively capture environmental changes and improve the adaptability of noise reduction.

[0097] It should be noted that after triggering the adjustment, the exponential moving average algorithm is used to smooth the feedback gain G and the phase compensation factor φ to avoid parameter mutations.

[0098] For example, in the above car scenario, the gain G suddenly changes from -5 dB to -7 dB. The algorithm retains the historical value with a weight of 0.7 and introduces the new value with a weight of 0.3 to generate a smoothed gain value of -5.6 dB. The phase factor φ is also smoothed and adjusted from 0.2 to 0.25. This smoothing process can reduce signal distortion during the adjustment process and maintain the naturalness of the speech.

[0099] Specifically, when dynamically adjusting the signal processing module according to the smoothed optimization parameter set, the gain and phase values can be updated in real time through a digital signal processor.

[0100] For example, the signal processing module adjusts the gain to -5.6 dB according to the smoothed parameters to suppress noise below 500 Hz, and at the same time adjusts the phase value to correct the phase distortion of the speech signal. The adjusted signal output parameters ensure that the output speech is clear and the background noise is significantly reduced. This dynamic adjustment can optimize the noise reduction effect in real time and improve the user experience.

[0101] It can be understood that the above embodiments form a logically rigorous noise reduction scheme through the complete process from signal acquisition to parameter adjustment. The implementation methods of each technical theme support each other to ensure the stable operation of the system in a dynamic noise environment.

[0102] For example, frequency domain analysis provides a basis for parameter generation, the Euclidean distance judges the timing of triggering adjustment, smoothing processing optimizes parameter transition, and dynamic adjustment ensures the final output quality. This multi-faceted collaborative scheme can effectively handle complex noise scenarios.

[0103] Step 104, perform adaptive noise reduction on the Bluetooth headset using the gain value, cut-off frequency value, and phase compensation value.

[0104] Obtain the feedback gain G and the cut-off frequency fc from the smoothing parameter set. Perform frequency-domain decomposition on the original audio signal through a digital signal processor to generate an initial noise reduction signal containing spectral features, and determine the initial noise reduction signal. If the spectral features of the initial noise reduction signal do not match the preset audio stream quality threshold, then adopt an adaptive filtering algorithm to dynamically adjust the feedback gain G and the cut-off frequency fc to obtain an adjusted noise reduction signal. According to the adjusted noise reduction signal, perform a time-domain correction operation through the digital signal processor in combination with the preset phase compensation factor φ to generate a time-domain optimized noise reduction signal, and determine the time-domain optimized noise reduction signal. Extract spectral features from the time-domain optimized noise reduction signal, and use the fast Fourier transform algorithm to generate a continuous audio stream, and determine the final noise reduction audio stream. Exemplarily, when the digital signal processor processes the original audio signal, an initial noise reduction signal can be generated through frequency-domain decomposition. Specifically, the processor performs a short-time Fourier transform on the input speech signal to extract spectral features, such as the amplitude information of each frequency band. Assume that in a conference room environment, the speech signal captured by the microphone is mixed with low-frequency noise from the operation of the air conditioner. The frequency-domain decomposition identifies that the noise below 200 Hz is dominant. The feedback gain G is set to -6 dB, and the cut-off frequency fc is set to 250 Hz. This decomposition method can quickly extract the spectral characteristics of the signal and provide a basis for subsequent noise reduction. In a possible implementation manner, when determining whether the spectral features of the initial noise reduction signal match the preset audio stream quality threshold, spectral smoothness analysis can be performed. For example, the preset threshold requires that the noise amplitude in the frequency band below 200 Hz is lower than -20 dB. If it is detected that the noise amplitude in this frequency band of the initial noise reduction signal is -15 dB, it indicates that the quality does not meet the standard and further adjustment is required. Adopt an adaptive filtering algorithm to dynamically optimize the feedback gain G and the cut-off frequency fc. For example, adjust G to -8 dB and fc to 220 Hz to enhance low-frequency noise suppression. This adaptive adjustment can accurately respond to changes in signal characteristics. It should be noted that the adjusted noise reduction signal needs to generate a time-domain optimized noise reduction signal through a time-domain correction operation. Specifically, the digital signal processor corrects the phase of the signal according to the preset phase compensation factor φ (such as 0.15). For example, in the above conference room scenario, the processor corrects the phase deviation introduced by noise processing in combination with φ according to the adjusted G and fc to ensure the time-domain continuity of the speech signal. This correction can improve the naturalness of the signal. Preferably, when extracting spectral features from the time-domain optimized noise reduction signal and generating the final audio stream, the fast Fourier transform algorithm can be used. For example, the processor converts the time-domain signal to the frequency domain, extracts the speech main frequency band features from 200 Hz to 4000 Hz, and generates a smooth audio stream. In the conference room scenario, the fast Fourier transform ensures that the output speech stream is continuous in the frequency spectrum and the background noise is significantly reduced. This method can efficiently generate a high-quality audio stream. It can be understood that the above embodiments constitute a complete noise reduction process through frequency-domain decomposition, adaptive filtering, time-domain correction, and fast Fourier transform.Each technical theme supports each other: frequency-domain decomposition provides signal features, adaptive filtering optimizes parameters, time-domain correction ensures signal naturalness, and fast Fourier transform generates the final output. This multi-faceted collaborative solution can effectively address the challenges of dynamic noise in a single scenario.

[0105] This application also discloses an adaptive noise reduction system for a Bluetooth headset, including: An acquisition module 21, configured to obtain a wearing offset value of the Bluetooth headset by using a speed change value and an angle change value of the Bluetooth headset; A determination module 22, configured to determine a frequency band sensitivity value corresponding to the wearing offset value; A calculation module 23, configured to calculate a gain value, a cut-off frequency value, and a phase compensation value corresponding to the frequency band sensitivity value by using an adaptive filtering algorithm; A noise reduction module 24, configured to perform adaptive noise reduction on the Bluetooth headset by using the gain value, the cut-off frequency value, and the phase compensation value. In some embodiments, the determination module 22 is configured to: Obtain a sound wave signal in the ear canal where the Bluetooth headset is worn; Use a frequency band decomposition algorithm to divide the sound wave signal in a preset frequency band into multiple sub-bands; Determine a degradation coefficient of the noise reduction performance of the multiple sub-bands; Use a sensitivity analysis algorithm to calculate, based on the degradation coefficient, the sensitivity values of the multiple sub-bands affected by the wearing offset value.

[0106] In some embodiments, the calculation module 23 is configured to: Use an adaptive filtering algorithm to calculate a gain value and a cut-off frequency value corresponding to the frequency band sensitivity value; If any one of the gain value and / or the cut-off frequency value is greater than a corresponding first preset threshold, use a fast Fourier transform algorithm to extract the phase information of the sub-band corresponding to the gain value and / or the cut-off frequency value; Use the phase information to calculate a phase compensation value.

[0107] In some embodiments, the determination module 22 is configured to: Obtain a sound field reflection coefficient corresponding to the sound wave signal; Use a sound field simulation algorithm to generate a geometric characteristic model of the sound field in the ear canal by combining the wearing offset value and the sound field reflection coefficient; Use the geometric characteristic model to obtain the ear canal depth and an updated reflection coefficient of the Bluetooth headset; If any one of the ear canal depth and / or the updated reflection coefficient is greater than a corresponding second preset threshold, use a frequency band decomposition algorithm to divide the sound wave signal in a preset frequency band into multiple sub-bands.

[0108] In some embodiments, the noise reduction module 24 is configured to: Determine a current parameter set corresponding to the Bluetooth headset before the speed change value and the angle change value occur, where the current parameter set includes a current gain value, a current cut-off frequency value, and a current phase compensation value; Determine the Euclidean distance between the current parameter set and a noise reduction parameter set, where the noise reduction parameter set includes the gain value, the cut-off frequency value, and the phase compensation value; If the Euclidean distance is greater than a third threshold, use an exponential moving average algorithm to smooth the gain value and the phase compensation value to obtain a smoothed gain value and a smoothed phase compensation value; Perform adaptive noise reduction on the Bluetooth headset by using the smoothed gain value, the cut-off frequency value, and the smoothed phase compensation value.

[0109] In some embodiments, the acquisition module 21 is configured to: The obtaining of the wearing offset value of the Bluetooth headset by using the speed change value and the angle change value of the Bluetooth headset includes: After the speed change value appears in the Bluetooth headset, collect the real-time angle change value of the Bluetooth headset through an acceleration sensor and a gyroscope; Fuse the angle data by using a Kalman filtering algorithm to obtain first angle data; Calibrate the first angle data by using a preset standard wearing position to determine the wearing offset value of the Bluetooth headset.

[0110] In some embodiments, the acquisition module 21 is configured to: collect an acoustic field feedback signal in the ear canal where the Bluetooth headset is worn through an ear canal microphone; perform noise reduction processing on the acoustic field signal by using an adaptive filter to obtain a first acoustic field signal; calculate an acoustic field reflection coefficient of the first acoustic field signal by using a linear regression algorithm.

[0111] Based on such an understanding, the technical solution of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.), and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods of various implementation scenarios of the present application.

[0112] Optionally, the above-mentioned physical device may further include a user interface, a network interface, a camera, a radio frequency (RF) circuit, sensors, an audio circuit, a WI-FI module, etc. The user interface may include a display screen (Display), an input unit such as a keyboard (Keyboard), etc. Optionally, the user interface may further include a USB interface, a card reader interface, etc. The network interface may optionally include a standard wired interface, a wireless interface (such as a WI-FI interface), etc.

[0113] Those skilled in the art can understand that the above-mentioned physical device structure provided in this embodiment does not constitute a limitation on the physical device, and may include more or fewer components, or combine certain components, or have different component arrangements.

[0114] Based on the above-mentioned Figure 1 As shown in the method, the embodiment of the present application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements the method corresponding to any embodiment. The storage medium may further include an operating system and a network communication module. The operating system is a program for managing the hardware and software resources of the above-mentioned physical device, and supports the operation of information processing programs and other software and / or programs. The network communication module is used to implement communication between components inside the storage medium, as well as communication between other hardware and software in the information processing physical device.

[0115] Through the description of the above embodiments, those skilled in the art can clearly understand that the present application can be implemented by means of software plus a necessary general hardware platform, or by hardware. By applying the solution of this embodiment, compared with the current existing technologies, this embodiment discloses an adaptive noise reduction method for a Bluetooth headset, uses the speed change value and angle change value of the Bluetooth headset to obtain the wearing offset value of the Bluetooth headset; determines the frequency band sensitivity value corresponding to the wearing offset value; adopts an adaptive filtering algorithm to calculate the gain value, cut-off frequency value and phase compensation value corresponding to the frequency band sensitivity value; uses the gain value, cut-off frequency value and phase compensation value to perform adaptive noise reduction on the Bluetooth headset. The present application can adjust the noise reduction parameters of the Bluetooth headset in real time according to the speed change and angle change conditions of wearing the Bluetooth headset, so as to maintain the reliability of the noise reduction effect and improve the user experience in different wearing situations.

[0116] It should be noted that in this document, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising" or any other variant thereof is intended to cover non-exclusive inclusion, such that a process, method, article, or device that comprises a series of elements includes not only those elements but also other elements not expressly listed, or elements that are inherent to such process, method, article, or device. Without further limitation, an element defined by the phrase "comprising an..." does not exclude the presence of additional identical elements in the process, method, article, or device that comprises the element.

[0117] The above are only specific embodiments of the present application, enabling those skilled in the art to understand or implement the present application. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to these embodiments described herein, but rather will conform to the broadest scope consistent with the principles and novel features claimed herein.

Claims

1. An adaptive noise reduction method for a Bluetooth headset, characterized in that, Including: Obtaining a wearing offset value of the Bluetooth headset by using a speed change value and an angle change value of the Bluetooth headset; Determining a frequency band sensitivity value corresponding to the wearing offset value; Calculating a gain value, a cut-off frequency value, and a phase compensation value corresponding to the frequency band sensitivity value by using an adaptive filtering algorithm; Performing adaptive noise reduction on the Bluetooth headset by using the gain value, the cut-off frequency value, and the phase compensation value.

2. The method according to claim 1, characterized in that The determining the frequency band sensitivity value corresponding to the wearing offset value includes: Obtaining a sound wave signal in the ear canal where the Bluetooth headset is worn; Dividing the sound wave signal in a preset frequency band into multiple sub-frequency bands by using a frequency band decomposition algorithm; Determining a degradation coefficient of the noise reduction performance of the multiple sub-frequency bands; Calculating a sensitivity value of the multiple sub-frequency bands affected by the wearing offset value based on the degradation coefficient by using a sensitivity analysis algorithm.

3. The method according to claim 2, wherein The calculating the gain value, the cut-off frequency value, and the phase compensation value corresponding to the frequency band sensitivity value by using an adaptive filtering algorithm includes: Calculating a gain value and a cut-off frequency value corresponding to the frequency band sensitivity value by using an adaptive filtering algorithm; If any one of the gain value and / or the cut-off frequency value is greater than a corresponding first preset threshold, extracting phase information of a sub-frequency band corresponding to the gain value and / or the cut-off frequency value by using a fast Fourier transform algorithm; Calculating a phase compensation value by using the phase information.

4. The method according to claim 2, wherein The dividing the sound wave signal in a preset frequency band into multiple sub-frequency bands by using a frequency band decomposition algorithm includes: Obtaining a sound field reflection coefficient corresponding to the sound wave signal; Generating a geometric characteristic model of the sound field in the ear canal by using a sound field simulation algorithm, aggregating the wearing offset value and the sound field reflection coefficient; Obtaining the ear canal depth and an updated reflection coefficient of the Bluetooth headset by using the geometric characteristic model; If any one of the ear canal depth and / or the updated reflection coefficient is greater than a corresponding second preset threshold, dividing the sound wave signal in a preset frequency band into multiple sub-frequency bands by using a frequency band decomposition algorithm.

5. The method according to claim 1, characterized in that, The performing the adaptive noise reduction on the Bluetooth headset by using the gain value, the cut-off frequency value, and the phase compensation value includes: Determining a current parameter set corresponding to the Bluetooth headset before the speed change value and the angle change value occur, the current parameter set including a current gain value, a current cut-off frequency value, and a current phase compensation value; Determining an Euclidean distance between the current parameter set and a noise reduction parameter set, the noise reduction parameter set including the gain value, the cut-off frequency value, and the phase compensation value; If the Euclidean distance is greater than a third threshold, performing smoothing processing on the gain value and the phase compensation value by using an exponential moving average algorithm to obtain a smoothed gain value and a smoothed phase compensation value; Performing adaptive noise reduction on the Bluetooth headset by using the smoothed gain value, the cut-off frequency value, and the smoothed phase compensation value.

6. The method according to claim 1, wherein The obtaining the wearing offset value of the Bluetooth headset by using the speed change value and the angle change value of the Bluetooth headset includes: After the speed change value appears in the Bluetooth headset, collecting the real-time angle change value of the Bluetooth headset through an acceleration sensor and a gyroscope; The Kalman filter algorithm is used to fuse the angle data to obtain the first angle data; The first angle data is calibrated using a preset standard wearing position to determine the wearing offset value of the Bluetooth headset.

7. The method according to claim 1, characterized in that, The obtaining of the acoustic wave signal in the ear canal wearing the Bluetooth headset includes: Collecting the sound field feedback signal in the ear canal wearing the Bluetooth headset through an ear canal microphone; Using an adaptive filter to denoise the sound field signal to obtain the first sound field signal; Using a linear regression algorithm to calculate the sound field reflection coefficient of the first sound field signal.

8. An adaptive noise reduction system for a Bluetooth headset, characterized in that, Including: An acquisition module for obtaining the wearing offset value of the Bluetooth headset by using the speed change value and the angle change value of the Bluetooth headset; A determination module for determining the frequency band sensitivity value corresponding to the wearing offset value; A calculation module for calculating the gain value, the cut-off frequency value, and the phase compensation value corresponding to the frequency band sensitivity value by using an adaptive filtering algorithm; A noise reduction module for performing adaptive noise reduction on the Bluetooth headset by using the gain value, the cut-off frequency value, and the phase compensation value.

9. An electronic device, characterized in that, Including: At least one processor; And A memory communicatively connected to the at least one processor; wherein, The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method according to any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, The computer program, when executed by a processor, implements the method according to any one of claims 1-7.

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