Mobile phone personalized sound effect adjusting method based on auricle form recognition

Abstract

The invention discloses a mobile phone personalized sound effect adjusting method based on auricle shape recognition, which comprises the following steps: acquiring three-dimensional shape data of a user auricle through a mobile phone camera, and extracting key parameters such as contour feature points, concave depth and reflection angle of the auricle; calculating a propagation path, a reflection characteristic and a resonance characteristic of sound waves in auricles by using an acoustic modeling technology, and generating a personalized sound effect optimization model; calculating auricle sound insulation characteristics based on an acoustic modeling result, wherein the auricle sound insulation characteristics comprise external noise shielding capability and specific frequency band sound wave absorption capability; when the user answers the call, audio optimization parameters are dynamically generated in combination with the spectral characteristics of the surrounding noise, the frequency band characteristics of the call audio and the personalized sound effect optimization model; dynamically adjusting the audio output setting of the mobile phone based on the audio optimization parameters; according to the method, deep combination of the auricle form and audio optimization is realized, and the personalized auditory experience of the user is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of mobile phones, and particularly to a method for personalized sound effect adjustment of mobile phones based on auricle shape recognition. Background Art

[0002] At present, mobile phone sound effect optimization technologies have been widely applied to scenarios such as phone calls, music playback, and video appreciation to enhance the user's auditory experience. Common technologies include audio equalization processing based on preset sound effect modes, noise suppression algorithms, and sound effect enhancement for specific scenarios. These methods are usually implemented by combining hardware and software, and process audio signals to optimize the output effect.

[0003] However, most of the existing technologies rely on generalized sound effect processing methods and lack in-depth adaptation to the auditory characteristics of individual users. Especially in complex acoustic environments, the effect of sound effect optimization often fails to meet the user's requirements for speech clarity, background noise suppression, and sound spatial sense. In addition, the existing optimization methods usually ignore the actual influence of the geometric shape of the user's auricle on the propagation of sound waves, resulting in low personalization and limited optimization effect.

[0004] Therefore, it is necessary to develop a method for personalized sound effect adjustment of mobile phones based on auricle shape recognition. Summary of the Invention

[0005] The present application provides a method for personalized sound effect adjustment of mobile phones based on auricle shape recognition to enhance the user's personalized auditory experience.

[0006] The present application provides a method for personalized sound effect adjustment of mobile phones based on auricle shape recognition, including: Obtaining three-dimensional shape data of the user's auricle through the mobile phone camera; extracting key auricle parameters including contour feature points, depression depth, and reflection angle of the auricle according to the three-dimensional shape data; Calculating the propagation path, reflection characteristics, and resonance characteristics of sound waves in the auricle by using acoustic modeling technology according to the key auricle parameters to generate a personalized sound effect optimization model; and calculating the sound insulation characteristics of the auricle according to the acoustic modeling results, where the sound insulation characteristics of the auricle include the ability to shield external noise and the ability to absorb sound waves in specific frequency bands; When the user answers a phone call, obtaining audio optimization parameters according to the spectral characteristics of the surrounding noise, the frequency band characteristics of the phone audio, and the personalized sound effect optimization model, where the audio optimization parameters include frequency response optimization parameters, gain distribution optimization parameters, and directivity enhancement parameters; Dynamically adjusting the audio output settings of the mobile phone based on the audio optimization parameters; Estimate the actual volume heard by the user based on the audio optimization parameters and the sound insulation characteristics of the auricle, in combination with the sound power of the mobile phone and the characteristics of external noise; Perform audio optimization according to the estimated actual volume heard by the user and in combination with the audio optimization mode selected by the user, including: in the clear mode, optimize the speech intelligibility by increasing the gain of the main speech frequency band; in the comfortable mode, reduce the auditory fatigue by dynamically limiting the volume change range; in the noise reduction mode, optimize the user's listening experience by weakening the gain of the noise frequency band.

[0007] Furthermore, based on the key auricle parameters, use acoustic modeling technology to calculate the propagation path, reflection characteristics, and resonance characteristics of sound waves in the auricle, and generate a personalized sound effect optimization model, including: Generate the three-dimensional morphological characteristics of the auricle based on the key auricle parameters, where the three-dimensional morphological characteristics include the curvature, depression depth, reflection angle, and spatial distribution of contour feature points of the auricle; According to the three-dimensional morphological characteristics of the auricle and in combination with the characteristics of the sound wave propagation medium, calculate the propagation path of sound waves in the auricle, where the propagation path includes the incident angle, reflection angle, and spatial distribution of reflection points of sound waves; Based on the propagation path, in combination with the absorption coefficient and sound wave frequency on the auricle surface, calculate the reflection intensity and absorption characteristics of the auricle, where the absorption characteristics include the absorption ratio of sound waves of different frequencies; Analyze the resonance characteristics of the auricle for sound waves of different frequency bands according to the reflection intensity and absorption characteristics, where the resonance characteristics include the resonance frequency and its gain amplitude of a specific frequency band; Integrate the propagation path data, reflection intensity, absorption characteristics, and resonance characteristics to generate a personalized sound effect optimization model; where the input of the personalized sound effect optimization model includes the spectral distribution of environmental noise, the frequency characteristics of telephone audio, and the three-dimensional morphological data of the auricle; the output of the personalized sound effect optimization model includes frequency response optimization parameters, gain distribution optimization parameters, and directivity enhancement parameters.

[0008] Furthermore, generating the three-dimensional morphological characteristics of the auricle based on the key auricle parameters includes: Use the method of multi-angle shooting to obtain the two-dimensional image data of the user's auricle through the mobile phone camera, where the image data includes the contour edge and surface texture characteristics of the auricle; Based on the two-dimensional image data, use image processing algorithms to extract the key feature points of the auricle, where the key feature points include contour edge points, depth points of the depression area, and curvature change points; According to the spatial distribution of the key feature points and in combination with the pose information of multi-angle images, use stereo vision algorithms to generate the initial point cloud data of the auricle, where the point cloud data includes the three-dimensional coordinate distribution of auricle surface feature points; Optimize the initial point cloud data, including removing outliers, filling sparse regions, and enhancing the point cloud density, to generate optimized high-density point cloud data; Based on the optimized high-density point cloud data, reconstruct the three-dimensional surface of the auricle using a surface fitting algorithm. The features of the three-dimensional surface include the curvature distribution, depression depth distribution, and surface normal information of the auricle; Extract three-dimensional morphological features from the features of the three-dimensional surface, including the curvature, depression depth, reflection angle, and spatial distribution of contour feature points of the auricle.

[0009] Furthermore, according to the three-dimensional morphological features of the auricle and combining with the characteristics of the sound wave propagation medium, calculate the propagation path of the sound wave in the auricle, including: Calculate the normal vector of the surface feature points of the auricle according to the three-dimensional morphological features of the auricle and the local curvature ; Calculate the incident angle of the sound wave according to the following formula (1) : ; where is the propagation direction vector of the sound wave, calculated from the positions of the sound wave source point and the auricle feature point; is the normal vector of the auricle surface feature point; Calculate the reflection direction of the sound wave according to the following formula (2): ; where is the reflection direction vector of the sound wave; is the propagation direction vector of the sound wave; is the normal vector of the auricle surface feature point; Calculate the spatial distribution of the reflection points according to the following formula (3): ; where is the spatial coordinate of the reflection point; is the incident point coordinate of the sound wave; is the reflection direction vector of the sound wave; is the correction factor, calculated according to the following formula (4): ; where is the local curvature of the auricle surface feature point.

[0010] Furthermore, based on the propagation path, combine the absorption coefficient and sound wave frequency on the auricle surface to calculate the reflection intensity and absorption characteristics of the auricle, including: Calculate the reflection intensity according to the following formula (5): ; wherein, is the reflection intensity of the feature point ; represents the absorption coefficient of the acoustic wave with a frequency of by the feature point on the auricle surface; is the local curvature of the feature point on the auricle surface; Calculate the absorption ratio according to the following formula (6): ; wherein, is the absorption ratio; represents the absorption coefficient of the acoustic wave with a frequency of by the feature point on the auricle surface.

[0011] Furthermore, analyzing the resonance characteristics of the auricle for acoustic waves in different frequency bands according to the reflection intensity and absorption characteristics includes: Extracting the key feature path length from the propagation path according to the three-dimensional morphological characteristics of the auricle , and the key feature path length includes the effective propagation distance of the acoustic wave from the incident point to the ear canal entrance after reflection; Calculate the resonance frequency according to the following formula (7) : ; wherein, is the resonance order of the resonance mode; is the propagation speed of the acoustic wave in the auricle; is the key feature path length; Calculate the resonance gain amplitude according to the following formula (8): ; wherein, is the gain amplitude of the auricle at a specific resonance frequency ; represents the reflection intensity at a specific resonance frequency ; represents the absorption ratio at a specific resonance frequency .

[0012] The beneficial effects of the technical solution provided by this application include: (1) Through a personalized sound effect optimization model generated based on the three-dimensional morphological data of the user's auricle, this application can adapt to the unique auricle characteristics of the user, dynamically adjust the audio output parameters, make the optimization effect of the audio signal more in line with the user's auditory habits, and significantly improve the sound quality experience. (2) Utilizing the sound insulation characteristics of the auricle (including the shielding ability against external noise and the absorption ability of sound waves in specific frequency bands) and the spectral characteristics of the surrounding noise, this application can dynamically suppress background noise, enhance the clarity of the target voice, and significantly improve the call experience of the user in complex environments. (3) By providing three audio optimization modes: clear mode, comfortable mode, and noise reduction mode, this application can dynamically adjust the sound effect according to the user's needs and environmental characteristics, enabling it to not only meet the requirements of voice enhancement but also reduce auditory fatigue or provide a noise reduction experience, and is applicable to a variety of application scenarios. (4) Combining the sound insulation characteristics of the auricle, audio optimization parameters, the sound power of the mobile phone, and the characteristics of external noise, this application can accurately estimate the volume actually heard by the user, and based on this, dynamically adjust the audio output settings to ensure that the user obtains the best volume perception effect while avoiding auditory damage caused by excessive volume. Description of the Drawings

[0013] Figure 1 is a flowchart of a method for adjusting personalized sound effects of a mobile phone based on auricle morphology recognition provided by the first embodiment of this application. Detailed Implementation Modes

[0014] Many specific details are set forth in the following description in order to provide a thorough understanding of this application. However, this application can be implemented in many other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the connotation of this application. Therefore, this application is not limited by the specific implementations disclosed below.

[0015] The first embodiment of this application provides a method for adjusting personalized sound effects of a mobile phone based on auricle morphology recognition. Please refer to Figure 1 , which is a schematic diagram of the first embodiment of this application. The following will be described in detail with reference to Figure 1 a method for adjusting personalized sound effects of a mobile phone based on auricle morphology recognition provided by the first embodiment of this application.

[0016] Step S101: Obtain the three-dimensional morphological data of the user's auricle through the mobile phone camera; according to the three-dimensional morphological data, extract the key auricle parameters including the contour feature points, depression depth, and reflection angle of the auricle.

[0017] The process of obtaining the three-dimensional morphological data of the user's auricle through the mobile phone camera includes the following specific steps: First, ensure that the mobile phone is equipped with a camera module with depth perception capabilities, such as a camera that supports structured light or TOF (Time of Flight) technology. The user needs to align the mobile phone camera with the auricle area, ensuring that the auricle is within the shooting range and keeping the entire contour of the auricle clearly visible. To improve the accuracy of data collection, it is recommended that the user take the shot under conditions of sufficient ambient light and appropriate background contrast to avoid distortion caused by too strong or too weak light.

[0018] During the data collection process, the camera generates multiple frames of two-dimensional images containing auricle contour information, and through the built-in image processing algorithm of the mobile phone, these images are preliminarily processed, including denoising, brightness equalization, and edge detection, to extract the auricle boundary contour line. Subsequently, through the depth perception function, combining the collected two-dimensional image data and the spatial positioning data of the camera, a three-dimensional point cloud model of the auricle is reconstructed. The point cloud model consists of the three-dimensional coordinates of discrete points on the auricle surface, reflecting the curved surface morphological characteristics of the auricle.

[0019] Extract the key feature parameters of the auricle from the generated point cloud model, specifically including contour feature points, depression depth, and reflection angle, etc. Contour feature points refer to the significant geometric points of the auricle in three-dimensional space, such as the edge points, bending points, and tip points of the auricle. These points are determined by calculating the point set with large curvature changes in the point cloud model. The depression depth refers to the vertical distance from the reference plane on the auricle surface to the concave area, and the reference plane can be obtained through the fitting calculation of the point cloud model. The reflection angle is the angle between the normal vector of the auricle surface and the incident direction of the sound wave, which is calculated based on the normal vector distribution of each point in the point cloud model and the preset sound wave direction.

[0020] In the above extraction process, a point cloud segmentation algorithm is needed to separate the effective area of the auricle from the background and noise data to ensure that only auricle-related data is retained. Subsequently, through the clustering and statistical analysis of the feature parameters, the key parameters are screened and optimized to generate a high-precision three-dimensional morphological data file of the auricle. Finally, the above data is stored in the device as the basis for subsequent acoustic modeling and personalized sound effect optimization.

[0021] Step S102: According to the key parameters of the auricle, use acoustic modeling technology to calculate the propagation path, reflection characteristics, and resonance characteristics of sound waves in the auricle, and generate a personalized sound effect optimization model; and calculate the sound insulation characteristics of the auricle according to the acoustic modeling results, and the sound insulation characteristics of the auricle include the ability to shield external noise and the ability to absorb sound waves in specific frequency bands.

[0022] In step S102, according to the key auricle parameters, the acoustic modeling technology is used to calculate the propagation path, reflection characteristics, and resonance characteristics of sound waves in the auricle, and further generate a personalized sound effect optimization model. At the same time, the sound insulation characteristics of the auricle are calculated according to the modeling results.

[0023] First, based on the key auricle parameters obtained from step S101, including contour feature points, depression depth, and reflection angle, a three-dimensional morphological model of the auricle is constructed. This model generates a high-precision geometric structure through a surface fitting method to describe the continuity and local geometric characteristics of the auricle surface. For acoustic modeling, the three-dimensional morphological model of the auricle is divided into multiple small units, each unit having clear physical properties, such as surface normal vector, absorption coefficient, and reflection coefficient. This division is usually achieved through finite element or boundary element methods to ensure the calculation accuracy of the sound wave propagation path.

[0024] In acoustic modeling, it is assumed that sound waves act on the auricle surface at different incident angles and frequencies. By tracking the path of sound waves from the incident point to the reflection point, the propagation direction, propagation distance of each path, and the final outgoing direction after multiple reflections on the auricle surface are determined. In this process, combined with the absorption coefficient and reflection coefficient of the auricle surface, the energy loss and direction change of sound waves during each reflection are calculated. The reflection characteristics include reflection intensity and reflection direction distribution, and by simulating the multiple reflection and scattering behaviors of sound waves, the processing ability of the auricle for sound waves of different frequencies is comprehensively described.

[0025] The resonance characteristics are obtained by analyzing the standing wave distribution and resonance effect of sound waves inside the auricle. For sound waves of a specific frequency, the geometric shape of the auricle may cause the energy of sound waves to increase or decrease in certain frequency bands. By analyzing the standing wave distribution of sound waves on specific paths on the auricle surface, the resonance frequency and the corresponding gain amplitude of the auricle can be determined. These data are obtained through numerical simulation and resonance characteristic analysis techniques and are used to describe the gain effect of the auricle on sound waves in the target frequency band.

[0026] Based on the above propagation path, reflection characteristics, and resonance characteristic data, a personalized sound effect optimization model is generated. This model is based on the geometric characteristics of the auricle and combines the sound energy attenuation characteristics, reflection distribution, and resonance gain of the propagation path to dynamically adjust the frequency response, gain distribution, and directivity enhancement parameters of sound waves to ensure that the sound effect optimization can adapt to the auricle characteristics of the user.

[0027] Meanwhile, based on the acoustic modeling results, the sound insulation characteristics of the auricle are calculated. The sound insulation characteristics include the ability to shield external noise and the ability to absorb sound waves in specific frequency bands. The ability to shield external noise is calculated by analyzing the multiple reflection and absorption behaviors in the sound wave propagation path, while the absorption ability in specific frequency bands is evaluated by simulating the absorption ratio of the auricle surface to sound waves of different frequencies. These sound insulation characteristic data will be used as input parameters for the personalized sound effect optimization model for subsequent sound effect adjustment and dynamic optimization.

[0028] The implementation of this step ensures the deep integration of the auricle shape and acoustic characteristics, providing precise technical support for subsequent audio optimization.

[0029] Furthermore, according to the key parameters of the auricle, using acoustic modeling technology to calculate the propagation path, reflection characteristics, and resonance characteristics of sound waves in the auricle, and generating a personalized sound effect optimization model, including: Based on the key parameters of the auricle, generate the three-dimensional morphological characteristics of the auricle. The three-dimensional morphological characteristics include the curvature of the auricle, the depth of the depression, the reflection angle, and the spatial distribution of the contour feature points. According to the three-dimensional morphological characteristics of the auricle, combined with the characteristics of the sound wave propagation medium, calculate the propagation path of sound waves in the auricle. The propagation path includes the incident angle, reflection angle, and spatial distribution of the reflection points of sound waves. Based on the propagation path, combined with the absorption coefficient of the auricle surface and the sound wave frequency, calculate the reflection intensity and absorption characteristics of the auricle. Among them, the absorption characteristics include the absorption ratio of sound waves of different frequencies. According to the reflection intensity and absorption characteristics, analyze the resonance characteristics of the auricle to sound waves in different frequency bands. The resonance characteristics include the resonance frequency and its gain amplitude in specific frequency bands. Integrate the propagation path data, reflection intensity, absorption characteristics, and resonance characteristics to generate a personalized sound effect optimization model. Among them, the input of the personalized sound effect optimization model includes the spectral distribution of environmental noise, the frequency characteristics of telephone audio, and the three-dimensional morphological data of the auricle. The output of the personalized sound effect optimization model includes frequency response optimization parameters, gain distribution optimization parameters, and directivity enhancement parameters.

[0030] In the process of generating a personalized sound effect optimization model according to the key parameters of the auricle, it is first necessary to generate its three-dimensional morphological characteristics using the key parameters of the auricle. The three-dimensional morphological characteristics of the auricle are obtained through geometric reconstruction of the spatial distribution of contour feature points, depth of depression, and reflection angle. Through known three-dimensional modeling algorithms, such as point cloud fitting technology, a surface model of the auricle is constructed. This model can accurately describe the curvature distribution of the auricle, the depth change of the depression area, and the positions of different feature points in three-dimensional space. These morphological characteristics are the basis for the sound wave propagation path and its interaction characteristics.

[0031] After the three-dimensional morphological features are generated, by combining the propagation characteristics of sound waves in air and the geometric structure of the auricle, the propagation path of sound waves is further calculated. The propagation path of sound waves includes the movement trajectory from the incident point on the auricle surface to the reflection point, as well as the angle after reflection and the spatial position of the reflection point. Specifically, the propagation path of sound waves is simulated by a ray tracing algorithm, which gradually calculates the multiple reflections and attenuations of sound waves based on the direction of the normal vector of the auricle surface and the initial parameters of sound wave incidence until the sound wave leaves the auricle or its energy decays to a preset threshold.

[0032] After obtaining the propagation path data, the reflection intensity and absorption characteristics of the auricle can be further calculated. The reflection intensity of the auricle depends on the scattering effect of the surface on the sound wave energy during each reflection, and this characteristic is affected by the surface curvature, material, and sound wave frequency. The absorption characteristics are determined by analyzing the energy loss on the auricle surface at specific frequencies, including the absorption ratios of low-frequency, medium-frequency, and high-frequency sound waves. The absorption characteristics are usually calculated by combining the absorption coefficient of the surface material and the energy change curve of the sound wave propagation path.

[0033] Based on the reflection intensity and absorption characteristics, the resonance characteristics of the auricle for sound waves in different frequency bands are further analyzed. The resonance characteristics are the enhancement effects of the auricle at certain specific frequencies, including the resonance frequency and its gain amplitude. By simulating the standing wave pattern of sound waves on the auricle surface, it can be determined which frequencies of sound waves will generate resonance gain inside the auricle. These characteristics are particularly important in audio optimization because they can significantly affect the user's perception of specific audio content.

[0034] Finally, the propagation path data, reflection intensity, absorption characteristics, and resonance characteristics are comprehensively input into an optimization model to generate a personalized audio effect optimization model. This model can not only dynamically match the physical characteristics of the user's auricle, but also generate specific optimization parameters by inputting the noise spectrum distribution of the external environment and the frequency characteristics of telephone audio. These parameters include frequency response optimization parameters for enhancing the user's sensitive frequency bands; gain distribution optimization parameters for adjusting the volume intensity of each frequency band; and directivity enhancement parameters for optimizing the spatial perception of sound. The result of this process is a complete audio effect optimization scheme that can dynamically adjust the audio output according to the user's auricle characteristics and environmental conditions, providing a personalized auditory experience for the user.

[0035] Furthermore, generating the three-dimensional morphological features of the auricle based on the key parameters of the auricle includes: Using the method of multi-angle shooting, obtaining two-dimensional image data of the user's auricle through a mobile phone camera, and the image data includes the contour edge and surface texture features of the auricle; Based on the two-dimensional image data, key feature points of the auricle are extracted using an image processing algorithm. The key feature points include contour edge points, depth points of the concave area, and curvature change points; According to the spatial distribution of the key feature points and combined with the pose information of the multi-angle images, an initial point cloud data of the auricle is generated using a stereo vision algorithm. The point cloud data includes the three-dimensional coordinate distribution of the surface feature points of the auricle; The initial point cloud data is optimized, including removing outliers, filling sparse areas, and increasing the point cloud density, to generate optimized high-density point cloud data; Based on the optimized high-density point cloud data, a three-dimensional surface of the auricle is reconstructed using a surface fitting algorithm. The features of the three-dimensional surface include the curvature distribution, concave depth distribution, and surface normal information of the auricle; Three-dimensional morphological features are extracted from the features of the three-dimensional surface, including the curvature, concave depth, reflection angle, and spatial distribution of the contour feature points of the auricle.

[0036] In the process of generating the three-dimensional morphological features of the auricle according to the key parameters of the auricle, first, two-dimensional image data of the user's auricle is obtained by taking multi-angle photos using a mobile phone camera. These image data need to cover the complete contour edge, surface texture features, and visible information of the concave area of the auricle, providing a basis for the subsequent three-dimensional morphological reconstruction. To ensure the integrity and accuracy of data collection, the user needs to adjust the head position according to the system prompt so that the camera can take photos of the auricle from multiple angles to ensure that all parts of the auricle can be covered under different perspectives.

[0037] After the image data collection is completed, the system analyzes each two-dimensional image through an image processing algorithm to extract the key feature points of the auricle. The key feature points include the contour edge points of the auricle, which are used to define the shape of the auricle; the depth points of the concave area, which are used to describe the geometric shape of the internal area of the auricle; and the curvature change points, which are used to reflect the bending and deformation characteristics of the auricle surface. The image processing algorithm can adopt edge detection, curvature estimation, and depth inference techniques to ensure that the extracted feature points have high accuracy and uniform distribution.

[0038] Next, according to the extracted key feature points and the pose information of the multi-angle images, an initial point cloud data of the auricle is generated using a stereo vision algorithm. The stereo vision algorithm calculates the position differences of the same feature point under different perspectives and deduces the three-dimensional coordinate distribution of these points, thereby constructing a preliminary three-dimensional structure of the auricle surface. The generated initial point cloud data includes a large number of discrete points describing the shape of the auricle surface, but may contain a certain number of errors or blank areas.

[0039] To further improve the quality of the point cloud data, the system performs optimization processing on the initial point cloud data. First, it removes the outlier points introduced due to data noise or algorithm errors to ensure the accuracy of the point cloud data. Then, it fills the sparse regions in the point cloud through an interpolation algorithm to make the point cloud cover every part of the auricle. Finally, by increasing the density of the point cloud, the geometric details of the auricle are presented with a higher resolution. The optimized point cloud data is a high-density data set describing the surface morphology of the auricle, laying the foundation for subsequent surface reconstruction.

[0040] After obtaining the optimized high-density point cloud data, the system processes the point cloud through a surface fitting algorithm to generate a three-dimensional surface model of the auricle. The surface fitting algorithm utilizes the spatial distribution information in the point cloud to construct a continuous surface of the auricle, ensuring that the model can truly reflect the geometric characteristics of the auricle. The generated three-dimensional surface model contains the curvature distribution, depression depth distribution, and surface normal information of the auricle. The curvature distribution reflects the degree of curvature of different regions on the auricle surface, the depression depth describes the geometric contour of the internal region of the auricle, and the surface normal information provides the direction characteristics of each part of the auricle.

[0041] From the generated three-dimensional surface model, the system further extracts the three-dimensional morphological features of the auricle. These features include the curvature of the auricle, which is used to describe the local curvature of the surface; the depression depth, which is used to quantify the geometric shape of the internal region of the auricle; the reflection angle, which is used to analyze the reflection behavior of sound waves on the auricle surface; and the spatial distribution of the contour feature points, which is used to describe the overall geometric structure of the auricle. These features constitute the three-dimensional morphological features of the auricle, providing accurate data support for subsequent acoustic modeling and sound effect optimization. This process ensures the integrity, accuracy, and usability of the auricle morphological data, meeting the requirements for personalized sound effect adjustment.

[0042] Furthermore, based on the three-dimensional morphological features of the auricle and combined with the characteristics of the sound wave propagation medium, calculating the propagation path of sound waves in the auricle includes: Calculating the normal vector of the feature points on the auricle surface according to the three-dimensional morphological features of the auricle and the local curvature ; Calculating the incident angle of the sound wave according to the following formula (1) : ; where is the propagation direction vector of the sound wave, calculated from the positions of the sound wave source point and the auricle feature point; is the normal vector of the auricle surface feature point; Calculating the reflection direction of the sound wave according to the following formula (2): ; where is the reflection direction vector of the sound wave; is the propagation direction vector of the sound wave; is the normal vector of the characteristic point on the auricle surface; Calculate the spatial distribution of the reflection points according to the following formula (3): ; where, is the spatial coordinate of the reflection point; is the coordinate of the sound wave incident point; is the reflection direction vector of the sound wave; is the correction factor, calculated according to the following formula (4): ; where, is the local curvature of the characteristic point on the auricle surface.

[0043] In order to calculate the propagation path of the sound wave in the auricle according to the three-dimensional morphological characteristics of the auricle, it is first necessary to obtain the normal vector of the characteristic point on the auricle surface and the local curvature . The characteristic points on the auricle surface are provided by the three-dimensional morphological model of the auricle, and the model generates surface data by point cloud fitting. The normal vector is the local normal direction of the characteristic point on the auricle surface, obtained by performing gradient analysis on the surface model at each characteristic point. The specific method is to use the method of local surface fitting to calculate the normal vector of the characteristic point to ensure the accuracy of the direction. The local curvature is calculated from the second derivative, reflecting the degree of local bending of the auricle surface. It is recommended to use the normalized value with the curvature range of to adapt to the correction factor in the subsequent formula.

[0044] Next, calculate the incident angle of the sound wave through formula (1). The incident angle represents the angle between the sound wave direction and the normal vector of the characteristic point, and the calculation formula is: ; where, is the propagation direction vector of the sound wave, calculated from the spatial coordinate difference between the sound wave source point and the auricle characteristic point : ; Here, and are the vectors and The modulus length is calculated by the Euclidean norm of a three-dimensional vector.

[0045] Then, the reflection direction of the sound wave is calculated using formula (2) . The reflection direction is based on the incident direction and the normal vector of the feature point and is calculated as follows: ; In the formula, is the dot product of the incident vector and the normal vector and is used to quantify the projection amount of in the direction. Through this formula, the direction of the reflection direction

[0046] is corrected to conform to the reflection law of sound waves, ensuring the physical rationality of the propagation path. According to formula (3), the spatial coordinates of the reflection point are derived from the incident point coordinates and the reflection direction vector ; Here, is the correction factor used to adjust the spatial position of the reflection point to ensure it conforms to the geometric characteristics of the auricle and the actual path of sound wave propagation.

[0047] The correction factor is given by formula (4): ; where is the local curvature of the surface feature points of the auricle. The larger the local curvature, the closer the reflection point is to the incident point; the smaller the local curvature, the farther the reflection point is from the incident point. This adjustment mechanism reflects the influence of the auricle surface on the sound wave propagation path through the physical characteristics of curvature.

[0048] Combining these formulas, the calculation steps of the entire sound wave propagation path ensure an accurate simulation of the reflection behavior on the auricle surface. By obtaining the three-dimensional morphological characteristics of the auricle and combining the characteristics of the sound wave propagation medium, the system can efficiently and accurately calculate the sound wave propagation path, including the incident angle, reflection direction, and spatial distribution of the reflection points. These calculation results provide important basic data for subsequent acoustic modeling and sound effect optimization, ensuring the accuracy and operability of the optimization process.

[0049] Furthermore, based on the propagation path, by combining the absorption coefficient and sound wave frequency on the auricle surface, the reflection intensity and absorption characteristics of the auricle are calculated, including: Calculate the reflection intensity according to the following formula (5): ; where is the reflection intensity of the sound wave with frequency at the characteristic point on the auricle surface .

[0050] represents the absorption coefficient of the sound wave with frequency at the characteristic point on the auricle surface . This coefficient reflects the ability of the auricle surface material to absorb sound wave energy at different frequencies. The specific acquisition method is through experimental data measurement, for example, recording the absorption rate of sound waves with different frequencies under known material conditions by an acoustic measurement device.

[0051] is the local curvature of the characteristic point on the auricle surface, representing the geometric shape of the auricle at this point. The local curvature is obtained through a curvature calculation algorithm from the three-dimensional morphological characteristics of the auricle. The specific method includes fitting the second derivative of the auricle point cloud data.

[0052] Calculate the absorption ratio according to the following formula (6): ; where is the absorption ratio, representing the absorption degree of the sound wave with frequency at the characteristic point on the auricle surface . represents the absorption coefficient of the sound wave with frequency at the characteristic point on the auricle surface.

[0053] Furthermore, analyze the resonance characteristics of the auricle for sound waves in different frequency bands according to the reflection intensity and absorption characteristics, including:[[]] Extract the key characteristic path length from the propagation path according to the three-dimensional morphological characteristics of the auricle , where the key characteristic path length includes the effective propagation distance of the sound wave from the incident point through reflection to the ear canal entrance; the key characteristic path length refers to the effective propagation distance of the sound wave from the incident point through reflection on the auricle surface to the ear canal entrance. This path length is calculated by tracking the propagation path. Specifically, the system needs to combine the three-dimensional morphological characteristics of the auricle, analyze the multiple reflection behaviors of the sound wave on the auricle surface, and match the sound wave propagation direction vector with the ear canal entrance direction vector to determine the final effective propagation distance.

[0054] Calculate the resonance frequency according to the following formula (7) : ; where is the harmonic order of the resonance mode; usually a positive integer, representing the standing wave mode of the sound wave. The recommended value is = 1 (fundamental mode) or = 2, 3 (higher-order modes), and the specific value depends on the auricle geometry and analysis requirements.

[0055] is the propagation speed of the sound wave in the auricle, with the unit of meters per second (m / s). This speed is determined by the characteristics of the propagation medium of the air in the auricle, and the general value range is 340 to 343 m / s (the speed of sound in air under standard atmospheric pressure and room temperature).

[0056] is the length of the key feature path, which has been obtained through propagation path tracing.

[0057] Formula (7) is based on the standing wave theory of acoustics and calculates the resonance frequency of the auricle through the relationship between the harmonic order, the speed of sound, and the propagation path length. By adjusting the harmonic order , the multiple resonance behaviors of the auricle to sound waves in different frequency bands can be analyzed.

[0058] According to the following formula (8), calculate the resonance gain amplitude: ; where is the gain amplitude of the auricle at a specific resonance frequency ; represents the reflection intensity of the auricle surface at a specific resonance frequency , which is calculated by the aforementioned formula (5).

[0059] represents the absorption ratio at a specific resonance frequency , which is calculated by the aforementioned formula (6).

[0060] Furthermore, by comprehensively considering the propagation path data, reflection intensity, absorption characteristics, and resonance characteristics, a personalized sound effect optimization model is generated, including: Based on the propagation path data, reflection intensity, absorption characteristics, and resonance characteristics, analyze the response characteristics of the auricle to sound waves in different frequency bands, and extract frequency response data and directivity characteristics; Combined with the three-dimensional morphological characteristics of the auricle and the spectral distribution of environmental noise, match the frequency characteristics of telephone audio, and generate optimization parameters for the target frequency band; According to the frequency response data and absorption characteristics, determine the frequency band characteristics that need to be compensated or enhanced, and generate frequency response optimization parameters; Combined with the optimization parameters of the target frequency band and the reflection intensity, determine the gain adjustment strategy for each frequency band, and generate gain distribution optimization parameters; Using the directivity characteristics and propagation path data, calculate the enhancement requirements for the sound wave reflection direction and generate directivity enhancement parameters.

[0061] To generate a personalized sound effect optimization model, it is first necessary to comprehensively analyze the propagation path data, reflection intensity, absorption characteristics, and resonance characteristics to understand the response characteristics of the auricle to sound waves in different frequency bands. The propagation path data provides the propagation trajectory of sound waves on the auricle surface, the position of reflection points, and the propagation distance. The reflection intensity and absorption characteristics respectively quantify the reflection ratio and energy loss of the auricle to sound waves. The resonance characteristics further reveal the standing wave behavior and energy amplification effect of the auricle at specific frequencies. By integrating these data, the physical response characteristics of the auricle to sound waves in each frequency band can be clearly described, from which frequency response data and directivity characteristics can be extracted. The frequency response data reflects the gain or attenuation trend of the auricle to sound waves in different frequency ranges, and the directivity characteristics describe how the auricle affects the spatial propagation of sound waves.

[0062] Next, combine the extracted auricle characteristics with the three-dimensional morphological characteristics and the spectral distribution of environmental noise to match the frequency characteristics of telephone audio. Telephone audio is usually concentrated in the speech frequency band (300 Hz to 3 kHz). The system determines the parts that need to be optimized in the target frequency band by analyzing the spectral characteristics of the noise and the frequency response data of the auricle. For example, in an environment with strong noise in a certain frequency band, the system will first identify the frequency band that has the greatest impact on speech clarity and generate optimization parameters for the target frequency band in combination with the absorption and reflection behaviors of the auricle.

[0063] Then, based on the frequency response data and absorption characteristics of the auricle, determine the frequency band characteristics that need to be compensated or enhanced, and thus generate frequency response optimization parameters. If there is a large absorption effect in certain frequency bands of the auricle, resulting in energy loss of the sound, the system will generate compensation parameters in these frequency bands to make up for the deficiency in auditory perception. Similarly, if the auricle shows a gain effect on sound waves in certain frequency bands (such as resonance frequency bands), the system will appropriately reduce the output intensity of these frequency bands to avoid over-amplification. Through this compensation and adjustment mechanism, ensure that the spectral output of the audio signal matches the user's auditory needs.

[0064] Subsequently, combining the optimization parameters of the target frequency band and the reflection intensity, the system further determines the gain adjustment strategy for each frequency band to generate gain distribution optimization parameters. The gain distribution optimization parameters dynamically adjust the signal output intensity by analyzing the reflection behavior of each frequency band. For example, for frequency bands with high reflection intensity, the system may reduce the gain to prevent distortion caused by multiple reflections of the signal; while for frequency bands with low reflection intensity, the system will increase the gain to compensate for the natural attenuation of the signal by the auricle. The core of this strategy is to dynamically allocate the output energy to ensure that the sound heard by the user is clear, balanced, and natural.

[0065] Finally, using the directivity characteristics and propagation path data, calculate the enhancement requirements for the sound wave reflection direction to generate directivity enhancement parameters. The directivity enhancement parameters dynamically adjust the spatial distribution of the sound wave output by analyzing the deviation between the reflection path of the sound wave on the auricle surface and the target direction. For example, when the directivity characteristics of the auricle point to a specific area, the system will preferentially enhance the audio signal in that direction to ensure the concentration of sound in the user's auditory perception. Through this spatial optimization strategy, the system can significantly enhance the sense of direction and three-dimensionality of the sound, especially in complex acoustic environments.

[0066] This model combines the physical characteristics of the auricle, environmental noise, and the characteristics of telephone audio, and can dynamically adjust the mobile phone audio output to better suit the user's personalized auditory needs.

[0067] Furthermore, calculating the sound insulation characteristics of the auricle according to the acoustic modeling results, the sound insulation characteristics of the auricle include the ability to shield external noise and the ability to absorb sound waves in specific frequency bands, including: According to the following formula (9), calculate the external noise shielding ability index: ; Where, is the shielding ability index of the auricle to external noise; Surface local curvature; The surface absorption coefficient for sound waves with a frequency of ; is the distance of the noise propagation path; is the energy distribution of the incident noise; The sound concentration coefficient of the concave area; represents the three-dimensional area of the auricle surface.

[0068] The shielding ability index of the auricle to external noise represents the degree of attenuation of the external noise energy by the auricle. The higher its value, the better the shielding effect of the auricle on noise.

[0069] : The surface local curvature of the auricle reflects the geometric bending degree of the auricle surface at each position. It is calculated through the three-dimensional shape model of the auricle.

[0070] : The absorption coefficient of the auricle surface for sound waves with a frequency of represents the energy absorption ratio of the auricle surface material to sound waves of different frequencies. It is obtained through experimental measurement, and the recommended value range is 0.1 to 0.9.

[0071] : The distance of the noise propagation path, which represents the distance from the noise source to the normal feature point on the auricle surface. It is obtained through the geometric analysis of the sound wave propagation path, and the recommended value is 10 to 50 millimeters.

[0072] The energy distribution of the incident noise, which represents the noise intensity received at different positions on the auricle surface. It is usually obtained by a noise measuring instrument, and the unit is the square of the sound pressure. 。

[0073] : The sound focusing coefficient of the auricle concave area, which represents the aggregation effect of the geometric shape of the auricle on the noise energy and depends on the auricle concave depth and shape. It is obtained by calculating the auricle shape data, and the recommended value range is 0.5 to 2.0.

[0074] : The three-dimensional area on the auricle surface, which is an integration range used to accumulate the noise shielding effect at each surface position.

[0075] The integration operation is completed on the auricle surface. By cumulatively calculating the energy shielding characteristics of the entire auricle area, the overall noise shielding ability index is obtained. 。

[0076] According to the following formula (10), calculate the absorption ability index of the sound wave in a specific frequency band: ; Among them, The absorption ability index of the sound wave with frequency ; Represents the absorption coefficient of the sound wave with frequency on the auricle surface; The reflected energy of the sound wave with frequency on the auricle surface is obtained by calculating the auricle reflection characteristics.

[0077] The reflection intensity of the surface to the sound wave with frequency represents the proportion of the sound wave reflected by the auricle surface, and the recommended value range is 0.2 to 0.8.

[0078] Local surface curvature.

[0079] Step S103: When the user answers the phone, obtain the audio optimization parameters according to the spectral characteristics of the surrounding noise, the frequency band characteristics of the phone audio, and the personalized sound effect optimization model. The audio optimization parameters include frequency response optimization parameters, gain distribution optimization parameters, and directional enhancement parameters.

[0080] In step S103, when the user answers a call, the system needs to collect the noise data of the surrounding environment and the frequency band characteristics of the telephone audio signal in real time, and combine them with the personalized sound effect optimization model to obtain the specific parameters for optimizing the sound effect.

[0081] First, the mobile phone uses the built-in microphone to detect the ambient noise in real time, and analyzes its spectral distribution, energy intensity, and interference direction. This process uses spectral analysis technology to decompose the noise signal into multiple frequency components, and determines the intensity of each frequency component through energy calculation. To more accurately evaluate the noise interference, the system also performs directional analysis on the detected noise signal to identify the direction of the main noise source, providing a basis for subsequent directional enhancement or suppression.

[0082] At the same time, the frequency band characteristics of the telephone audio signal are determined by analyzing the frequency distribution, time-domain amplitude change, and voice content characteristics of the call signal. The system uses signal processing algorithms to extract the main frequency band information of the voice, identify the key frequency range of the call signal and the non-voice frequency components, ensuring that the optimization target focuses on improving the voice clarity and the performance of the voice frequency band.

[0083] Subsequently, combined with the personalized sound effect optimization model of the auricle, the audio optimization parameters are calculated. The personalized sound effect optimization model contains the frequency response, gain requirements, and directional characteristic data of the user's auricle. By matching the spectral distribution of the surrounding noise and the frequency band characteristics of the telephone audio signal, the system dynamically adjusts the optimization requirements of specific frequency bands of the auricle. For example, for the voice frequency band in the telephone signal, the system preferentially matches the frequency response characteristics of the auricle in this frequency band to generate frequency response optimization parameters. These parameters optimize the user's auditory experience by compensating for the deficiencies of the auricle in sensitive frequency bands or enhancing its resonance characteristics.

[0084] The generation of the gain distribution optimization parameters is based on the spectral characteristics of the noise signal. The system suppresses the frequency range where the ambient noise is mainly distributed, and at the same time enhances the voice-related frequency bands in the telephone audio signal, making the voice more prominent in a complex environment. The gain distribution optimization parameters not only ensure the enhancement of the voice, but also effectively reduce the auditory fatigue of the user caused by noise interference.

[0085] The directional enhancement parameters are calculated by combining the noise direction and the directional characteristics of the auricle. The system dynamically adjusts the spatial distribution of the audio signal according to the direction of the noise source, weakening the signal in the interference direction and optimizing the sound performance in the target direction. This processing enables the user to more concentratively perceive the voice of the call object, enhancing the spatial positioning and clarity of the call.

[0086] In summary, in step S103, by collecting and analyzing the characteristics of environmental noise and telephone audio signals in real time, and combining with the personalized sound effect optimization model of the user's auricle, audio optimization parameters including frequency response optimization parameters, gain distribution optimization parameters, and directivity enhancement parameters are generated. These parameters provide precise data support for the subsequent dynamic adjustment of audio output and ensure that the audio signal can be optimized according to the characteristics of the user's auricle.

[0087] Step S104: Dynamically adjust the audio output settings of the mobile phone based on the audio optimization parameters.

[0088] In step S104, the process of dynamically adjusting the audio output settings of the mobile phone based on the audio optimization parameters involves multiple real-time calculations and signal processing steps to ensure that the output audio can best match the characteristics of the user's auricle and the current environmental conditions.

[0089] First, the frequency response optimization parameters are used to adjust the amplitude distribution of the audio signal in different frequency ranges. The system adjusts the equalizer in the digital signal processing module to perform gain or attenuation operations on the signals in different frequency bands, so that the optimized spectrum matches the frequency response characteristics of the user's auricle. Specifically, for the characteristics that the user's auricle is sensitive to sound waves in certain frequency bands, the system will correspondingly increase the output energy of these frequency bands; for the frequency bands that are easily absorbed or attenuated by the auricle, the system compensates and enhances the output intensity of this frequency band to ensure the balance and clarity of the listening experience.

[0090] Second, the gain distribution optimization parameters are used to dynamically adjust the overall volume ratio of the audio output. In this process, the system dynamically allocates the output power according to the importance of the target frequency band. For example, for the voice frequency band (usually concentrated in the range of 300 Hz to 3 kHz), the system will preferentially increase the gain to enhance the clarity of the voice, while for the secondary frequency band or the noise-related frequency band, the system reduces the gain to reduce interference to the user. The adjustment of the gain distribution needs to respond in real time to the sound insulation characteristics of the user's auricle, the intensity of environmental noise, and the characteristics of the current audio content.

[0091] At the same time, the directivity enhancement parameters are used to control the spatial distribution of the audio signal. By adjusting the sound emission direction of the speaker or the synthesis of the virtual sound field, the system can enhance the sound direction that the user's auricle can effectively gather. For example, in a noisy environment, if the noise source is in a direction that the auricle cannot shield, the system will optimize the audio signal in the target direction through directivity enhancement technology, making the call or audio content perceived by the user more concentrated and clear. The implementation of directivity enhancement can be completed through beamforming technology, combined with the geometric characteristics of the user's auricle and the current noise source direction.

[0092] All of the above adjustments are coordinated by the real-time signal processing module to ensure that the audio output settings can dynamically respond to the user's auricle characteristics and environmental changes. In addition, to ensure the smoothness of the adjustment, the system continuously monitors the changes in the environment and audio signals, and recalculates the optimization parameters and adjusts the output settings at a fixed refresh frequency (e.g., 50 ms). Through this dynamic adjustment method, the audio output of the mobile phone can not only meet the user's personalized auditory needs but also adapt to complex and changing environmental conditions. Ultimately, the user obtains a clearer, more balanced, and more comfortable auditory experience.

[0093] Furthermore, based on the audio optimization parameters, dynamically adjusting the audio output settings of the mobile phone includes: According to the frequency response optimization parameters, for the specific resonance frequency bands of the user's auricle, dynamically adjust the frequency distribution of the audio signal, so as to enhance the signal intensity in the sensitive frequency bands of the user's auricle, and at the same time compensate for the signal energy in the frequency bands that are easily absorbed by the auricle; According to the gain distribution optimization parameters, combined with the sound output power of the mobile phone, dynamically adjust the output gain of each audio frequency band, increase the gain in the target frequency band to optimize the clarity or immersion of the audio output, and at the same time reduce the gain in the non-critical frequency bands to reduce energy waste or interference; According to the directivity enhancement parameters, combined with the directivity characteristics of the user's auricle, optimize the signal output of the mobile phone speaker, so as to enhance the audio signal in the target direction and attenuate the sound in other directions appropriately.

[0094] To dynamically adjust the audio output settings of the mobile phone, first, it is necessary to dynamically adjust the frequency distribution of the audio signal according to the frequency response optimization parameters. The core of this process is to identify the specific resonance frequency bands and sensitive frequency bands of the user's auricle. The resonance frequency band refers to the frequency band in which the auricle shows an enhanced effect on sound waves at a specific frequency, and the sensitive frequency band refers to the area where the user perceives the sound in this frequency range more strongly. Based on these parameters, within the resonance frequency band, the system enhances the signal intensity by appropriately adjusting the frequency distribution of the audio signal, so as to make full use of the natural amplification effect of the auricle and improve the clarity and penetration of the audio. In the frequency bands that are easily absorbed by the auricle, the system will increase the signal energy in this frequency band through a compensation strategy to make up for the weakening of perception caused by auricle absorption, ensuring the balance and naturalness of the audio signal across the entire frequency spectrum.

[0095] Secondly, according to the gain distribution optimization parameters, the system needs to dynamically adjust the output gain of each audio frequency band in combination with the sound - emitting power of the mobile phone. The core of the gain adjustment is to increase the gain for the target frequency band to optimize the clarity or immersion of the audio output. For example, in a voice - call scenario, the system will preferentially enhance the gain of the main voice frequency bands (such as 300 Hz to 3 kHz), making the call clearer. In an entertainment scenario, such as music playback or game sound effects, the system will enhance the gain of the low - frequency and high - frequency bands to improve the impact of the low - frequency and the detail performance of the high - frequency. At the same time, in non - critical frequency bands, the system appropriately reduces the gain to reduce unnecessary energy consumption and potential auditory interference. This dynamic gain distribution adjustment not only optimizes the user's auditory experience but also improves the energy efficiency of the device.

[0096] Finally, according to the directivity enhancement parameters, the system combines the directivity characteristics of the user's auricle to optimize the signal output of the mobile phone speaker. The directivity characteristic of the auricle refers to the aggregation and shielding effects of the auricle on sound waves from different directions. By analyzing the directivity enhancement parameters of the auricle, the system can dynamically adjust the signal output direction of the speaker. For example, in a specific direction, the system enhances the output intensity of the audio signal, making the sound more concentrated in that direction to meet the user's auditory needs. At the same time, the system can appropriately attenuate the audio signal in other non - target directions to reduce environmental noise interference or prevent sound leakage. This directivity - based optimization strategy is particularly applicable to voice calls in noisy environments, private music listening, or immersive audio - visual experiences.

[0097] Through frequency - distribution adjustment, dynamic gain adjustment, and directivity optimization, the system realizes a full - range dynamic adjustment of the audio output settings, enabling the audio signal to better adapt to the physiological characteristics of the user's auricle and the requirements of the current usage scenario. This method not only improves the audio experience but also enhances the sound performance and energy utilization efficiency of the device, ensuring the operability of the system and the auditory comfort of the user.

[0098] Step S105: Estimate the volume actually heard by the user according to the audio optimization parameters and the auricle sound - insulation characteristics, in combination with the sound - emitting power of the mobile phone and the characteristics of external noise.

[0099] In step S105, the process of estimating the volume actually heard by the user according to the audio optimization parameters and the auricle sound - insulation characteristics, in combination with the sound - emitting power of the mobile phone and the characteristics of external noise, involves the integration and real - time calculation of multiple data items.

[0100] First, the mobile phone obtains the audio signal output characteristics of each current frequency band according to the frequency response optimization parameter and the gain distribution optimization parameter in the audio optimization parameters, including the signal strength of each frequency band and the corresponding gain adjustment value. Subsequently, in combination with the noise shielding ability and the sound wave absorption ability of specific frequency bands in the auricle sound insulation characteristics, the actual attenuation degree of the sound waves emitted by the mobile phone by the auricle is calculated. The shielding ability determines the interference weakening ratio of external noise at the auricle entrance, while the absorption ability is used to analyze the energy loss of the auricle for signals of different frequencies.

[0101] At the same time, the system collects the current sound power data of the mobile phone, and comprehensively evaluates the total sound pressure level of the sound wave at the auricle entrance in combination with the spectral intensity and directional distribution in the external noise characteristics. For external noise, the system detects the noise spectral characteristics of the surrounding environment through a microphone, matches the frequency components of the noise signal with the sound insulation characteristics of the user's auricle, and determines which frequency bands of the noise signal can directly enter the ear canal through the shielding effect of the auricle. For the noise signal entering the ear canal, the system further combines the absorption ability of the auricle for sound waves of specific frequency bands to calculate the actual attenuation effect of the noise signal inside the auricle.

[0102] After completing the above calculations of the propagation of sound waves and noise signals, the system calculates the actual auditory perception signal of the user inside the auricle by comparing the sound pressure levels of the mobile phone sound signal and the remaining noise signal. Specifically, after the sound pressure level of the mobile phone sound signal is processed by the gain or attenuation of the auricle, it is superimposed with the noise signal inside the ear canal to form the volume that the user finally actually hears.

[0103] This process is dynamic. The system continuously monitors the changes in the intensity and spectral distribution of external noise, adjusts the influence of the auricle sound insulation characteristics on the signal propagation path in real time, and re-estimates the initial intensity and gain distribution of the signal according to the change in the sound power of the mobile phone. Finally, the volume that the user actually hears is generated by the system after integrating all data, and based on this, precise input is provided for subsequent audio optimization steps. In this way, the system can ensure that the audio signal heard by the user is not only clear, but also can adapt to the noise interference in different environments and meet the personalized needs of multiple scenarios.

[0104] Furthermore, estimating the volume actually heard by the user according to the audio optimization parameters and the auricle sound insulation characteristics, in combination with the sound power of the mobile phone and the external noise characteristics, includes: Calculating the volume actually heard by the user according to the following formula (11): ; Where represents the volume actually heard by the user at frequency ; is the interference data of external noise; is the sound pressure correction value inside the auricle; interference data of external noise It is calculated by the following formula (12): ; wherein, represents the spectral intensity of external noise, expressed as the sound pressure level at a specific frequency This data can be obtained by recording external noise with an environmental microphone and combining it with a spectral analysis tool.

[0105] is the external noise shielding ability index, calculated by formula (9), and is used to quantify the noise shielding effect of the auricle.

[0106] The sound pressure correction value inside the auricle is calculated by the following formula (13): ; wherein, is the absorption ratio of the auricle to the sound wave with a frequency of , calculated by formula (6), and a higher value indicates stronger absorption.

[0107] is the reflection intensity of the auricle to the sound wave with a frequency of , obtained by calculation with formula (5); is the local curvature of the auricle surface; is the initial sound pressure value of the sound wave at the auricle entrance, calculated according to the following formula (14): ; wherein, is the sound power of the mobile phone, in watts (W), representing the power output of the mobile phone speaker at a specific frequency.

[0108] is the gain distribution optimization parameter, representing the gain ratio of the sound wave with a frequency of ; is the effective area of the auricle entrance, in square meters (m²), representing the geometric cross-sectional area of the auricle entrance, which can be measured through the three-dimensional modeling data of the auricle.

[0109] Step S106: According to the estimated actual volume heard by the user and combined with the audio optimization mode selected by the user, perform audio optimization, including: in the clear mode, optimize the speech intelligibility by increasing the gain of the main speech frequency band; in the comfortable mode, reduce the auditory fatigue by dynamically limiting the volume change range; in the noise reduction mode, optimize the user's listening experience by weakening the gain of the noise frequency band.

[0110] In step S106, based on the estimated volume actually heard by the user and combined with the audio optimization mode selected by the user, the system further optimizes the audio signal to adapt to the user's needs and specific scenarios. The audio optimization modes are divided into a clear mode, a comfort mode, and a noise reduction mode. The optimization objectives and implementation strategies of each mode are different, and the system dynamically adjusts the corresponding signal processing parameters according to the volume actually heard by the user.

[0111] In the clear mode, the optimization objective of the system is to improve the intelligibility of speech, especially in noisy environments or when the user's hearing may be restricted. According to the volume actually heard, the system preferentially enhances the gain of the main speech frequency band, which typically includes the range from 300 Hz to 3 kHz. This is the key frequency band for speech clarity and intelligibility. The system enhances the signal in this frequency band through a digital equalizer, while moderately suppressing the volume of the non-speech frequency bands to highlight the speech content. This mode also combines the directional enhancement parameters of the auricle to optimize the spatial distribution of the speech signal, making the sound perceived by the user more concentrated in the target direction, thereby further improving speech clarity.

[0112] In the comfort mode, the optimization focus of the system is to reduce auditory fatigue, especially in scenarios of long-term calls or continuous audio playback. According to the volume actually heard by the user, the system dynamically limits the fluctuation range of the volume to avoid auditory discomfort caused by overly strong or frequently changing signals. Through automatic gain control technology, the system maintains the audio output within a comfortable volume range, while restricting the excessive enhancement of the high-frequency and low-frequency bands to make the overall sound smoother and more natural. The comfort mode also combines the sound insulation characteristics of the user's auricle to appropriately compensate for the signal in the frequency bands that are easily absorbed by the auricle to ensure auditory balance.

[0113] In the noise reduction mode, the optimization objective of the system is to reduce the interference of environmental noise on the user's listening experience. According to the estimated volume actually heard by the user, the system identifies the frequency band distribution of the external noise and dynamically weakens the gain of these frequency bands to maximize the suppression of the impact of background noise. The system utilizes the shielding ability of the auricle and the results of noise directional analysis to attenuate the signal in the direction of the noise source, while enhancing the audio signal in the target frequency band, enabling the user to hear the target sound content more clearly. In addition, the noise reduction mode also specifically optimizes the low-frequency environmental noise and uses a noise reduction filter to weaken the interference of low-frequency noise.

[0114] During the implementation of the above three modes, the system will adjust the audio optimization parameters in real time according to the actual volume heard by the user and the environmental changes to ensure that the output audio effect always meets the user's needs. Each optimization adjustment of the system is completed with extremely low latency through the signal processing module to ensure the synchronization of the optimization effect with the user's perception. By dynamically combining the estimated volume and the mode selected by the user, the system can provide accurate, efficient, and personalized audio optimization services, enabling the user to obtain the best auditory experience in different usage scenarios.

[0115] The second embodiment of the present application provides an electronic device, which includes: A processor; A memory for storing a program, which, when read and executed by the processor, executes a method for adjusting the personalized sound effect of a mobile phone based on auricle morphology provided in the first embodiment of the present application.

[0116] The third embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, it executes a method for adjusting the personalized sound effect of a mobile phone based on auricle morphology provided in the first embodiment of the present application.

[0117] Although the present application is disclosed above with preferred embodiments, it is not intended to limit the present application. Any person skilled in the art can make possible changes and modifications without departing from the spirit and scope of the present application. Therefore, the protection scope of the present application should be determined by the scope defined by the claims of the present application.

Claims

1. A method for adjusting personalized sound effects of a mobile phone based on auricle morphology recognition, characterized in that: include: Acquire the three-dimensional shape data of the user's auricle through the mobile phone camera; Extracting key parameters of the auricle including contour feature points, depression depth and reflection angle of the auricle according to the three-dimensional morphological data; According to the key parameters of the auricle, the propagation path, reflection characteristics and resonance characteristics of the sound waves in the auricle are calculated using acoustic modeling technology to generate a personalized sound effect optimization model; and calculating the auricle sound insulation characteristics according to the acoustic modeling results, wherein the auricle sound insulation characteristics include the ability to shield external noise and the ability to absorb sound waves in a specific frequency band; When the user answers a call, audio optimization parameters are obtained according to the spectrum characteristics of the surrounding noise, the frequency band characteristics of the telephone audio, and the personalized sound effect optimization model, wherein the audio optimization parameters include frequency response optimization parameters, gain distribution optimization parameters, and directionality enhancement parameters; Dynamically adjusting the audio output settings of the mobile phone based on the audio optimization parameters; The volume actually heard by the user is estimated based on the audio optimization parameters and the auricle sound insulation characteristics, combined with the sound power of the mobile phone and the external noise characteristics; Perform audio optimization based on the estimated volume actually heard by the user and in combination with the audio optimization mode selected by the user, including: in the clear mode, optimizing speech intelligibility by increasing the gain of the main frequency band of speech; In comfort mode, auditory fatigue is reduced by dynamically limiting the volume change range; in noise reduction mode, the user's hearing experience is optimized by weakening the gain of the noise frequency band.

2. The method for adjusting personalized sound effects of a mobile phone based on auricle morphology recognition according to claim 1, characterized in that: The method of calculating the propagation path, reflection characteristics and resonance characteristics of sound waves in the auricle using acoustic modeling technology according to the key parameters of the auricle to generate a personalized sound effect optimization model includes: Based on the key parameters of the auricle, generating three-dimensional morphological features of the auricle, the three-dimensional morphological features including the curvature, the depth of the depression, the reflection angle and the spatial distribution of the contour feature points of the auricle; According to the three-dimensional morphological characteristics of the auricle and the propagation medium characteristics of the sound wave, the propagation path of the sound wave in the auricle is calculated, and the propagation path includes the incident angle, reflection angle and spatial distribution of the reflection point of the sound wave; Based on the propagation path, combined with the absorption coefficient of the auricle surface and the sound wave frequency, the reflection intensity and absorption characteristics of the auricle are calculated, wherein the absorption characteristics include the absorption ratio of sound waves of different frequencies; Analyzing the resonance characteristics of the auricle to sound waves in different frequency bands according to the reflection intensity and absorption characteristics, wherein the resonance characteristics include the resonance frequency and gain amplitude of a specific frequency band; The propagation path data, reflection intensity, absorption characteristics and resonance characteristics are integrated to generate a personalized sound optimization model; wherein the input of the personalized sound optimization model includes the spectral distribution of ambient noise, the frequency characteristics of telephone audio and the three-dimensional morphological data of auricle; the output of the personalized sound optimization model includes frequency response optimization parameters, gain distribution optimization parameters and directionality enhancement parameters.

3. The method for adjusting personalized sound effects of a mobile phone based on auricle morphology recognition according to claim 2, characterized in that: The step of generating the three-dimensional morphological features of the auricle based on the auricle key parameters includes: Using a multi-angle shooting method, the two-dimensional image data of the user's auricle is obtained through the mobile phone camera, and the image data includes the contour edge and surface texture features of the auricle; Based on the two-dimensional image data, an image processing algorithm is used to extract key feature points of the auricle, wherein the key feature points include contour edge points, depth points of the concave area, and curvature change points; According to the spatial distribution of the key feature points, combined with the position information of the multi-angle images, the initial point cloud data of the auricle is generated by using a stereo vision algorithm, wherein the point cloud data includes the three-dimensional coordinate distribution of the feature points on the surface of the auricle; Optimizing the initial point cloud data, including removing outliers, filling sparse areas, and increasing point cloud density, to generate optimized high-density point cloud data; Based on the optimized high-density point cloud data, a surface fitting algorithm is used to reconstruct the three-dimensional surface of the auricle, wherein the characteristics of the three-dimensional surface include the curvature distribution, the concave depth distribution and the surface normal information of the auricle; Three-dimensional morphological features including the curvature of the auricle, the depth of the depression, the reflection angle and the spatial distribution of the contour feature points are extracted from the features of the three-dimensional surface.

4. The method for adjusting personalized sound effects of a mobile phone based on auricle morphology recognition according to claim 2, characterized in that: The step of calculating the propagation path of the sound wave in the auricle according to the three-dimensional morphological characteristics of the auricle and the propagation medium characteristics of the sound wave comprises: According to the three-dimensional morphological characteristics of the auricle, the normal vector of the feature points on the auricle surface is calculated and local curvature ; According to the following formula (1), the incident angle of the sound wave is calculated : ； in, is the propagation direction vector of the sound wave, which is calculated from the positions of the sound wave source point and the auricle feature point; is the normal vector of the feature point on the auricle surface; According to the following formula (2), the reflection direction of the sound wave is calculated: ； in, is the reflection direction vector of the sound wave; is the propagation direction vector of the sound wave; is the normal vector of the feature point on the auricle surface; According to the following formula (3), the spatial distribution of reflection points is calculated: ； in, is the spatial coordinate of the reflection point; is the coordinate of the incident point of the sound wave; is the reflection direction vector of the sound wave; is the correction factor, calculated according to the following formula (4): ； in, The local curvature of the feature points on the auricle surface.

5. The method for adjusting personalized sound effects of a mobile phone based on auricle morphology recognition according to claim 4, characterized in that: The calculation of the reflection intensity and absorption characteristics of the auricle based on the propagation path and in combination with the absorption coefficient of the auricle surface and the sound wave frequency includes: According to the following formula (5), the reflection intensity is calculated: ； in, For feature points The reflection intensity; The frequency of feature points on the auricle surface is The absorption coefficient of sound waves; is the local curvature of the feature point on the auricle surface; The absorption ratio is calculated according to the following formula (6): ； in, is the absorption ratio; The frequency of feature points on the auricle surface is The absorption coefficient of sound waves.

6. The method for adjusting personalized sound effects of a mobile phone based on auricle morphology recognition according to claim 5, characterized in that: The step of analyzing the resonance characteristics of the auricle to sound waves of different frequency bands according to the reflection intensity and absorption characteristics includes: According to the three-dimensional morphological characteristics of the auricle, the key characteristic path length is extracted from the propagation path , the key characteristic path length includes the effective propagation distance of the sound wave from the incident point to the entrance of the ear canal after reflection; According to the following formula (7), the resonant frequency is calculated : ； in, is the resonance order of the resonance mode; is the speed of sound waves in the auricle; is the key characteristic path length; According to the following formula (8), the resonance gain amplitude is calculated: ； in, The ear pinna at a specific resonant frequency The gain amplitude at Indicates that at a specific resonant frequency The reflection intensity under Indicates that at a specific resonant frequency The absorption ratio below.

7. The method for adjusting personalized sound effects of a mobile phone based on auricle morphology recognition according to claim 6, characterized in that: The step of integrating the propagation path data, reflection intensity, absorption characteristics and resonance characteristics to generate a personalized sound optimization model includes: Based on the propagation path data, reflection intensity, absorption characteristics and resonance characteristics, the response characteristics of the auricle to sound waves in different frequency bands are analyzed, and the frequency response data and directional characteristics are extracted; Combine the three-dimensional morphological characteristics of the auricle and the spectrum distribution of the ambient noise to match the frequency characteristics of the telephone audio and generate the optimized parameters of the target frequency band; Determine the frequency band characteristics that need to be compensated or enhanced according to the frequency response data and the absorption characteristics, and generate frequency response optimization parameters; Combine the optimization parameters and reflection intensity of the target frequency band to determine the gain adjustment strategy of each frequency band and generate the gain distribution optimization parameters; Using the directivity characteristics and propagation path data, the enhancement requirements in the sound wave reflection direction are calculated and the directivity enhancement parameters are generated.

8. The method for adjusting personalized sound effects of a mobile phone based on auricle morphology recognition according to claim 7, characterized in that: The auricle sound insulation characteristics are calculated according to the acoustic modeling results, and the auricle sound insulation characteristics include the ability to shield external noise and the ability to absorb sound waves in a specific frequency band, including: According to the following formula (9), the external noise shielding capability index is calculated: ； in, It is the shielding ability index of the auricle against external noise; Local curvature of the surface; The surface frequency is The sound absorption coefficient of is the distance of the noise propagation path; is the energy distribution of the incident noise; The sound concentration coefficient of the concave area; Represents the three-dimensional area of ​​the auricle surface; According to the following formula (10), the absorption capacity index of sound waves in a specific frequency band is calculated: ； in, Frequency The sound wave absorption capacity index; The surface of the auricle is The absorption coefficient of sound waves; The surface of the auricle is affected by the frequency The reflected energy of the sound waves; Surface to frequency The intensity of the reflection of the sound waves; Local curvature of the surface.

9. The method for adjusting personalized sound effects of a mobile phone based on auricle morphology recognition according to claim 8, characterized in that: The estimating the volume actually heard by the user according to the audio optimization parameter and the auricle sound insulation characteristic, combined with the sound power of the mobile phone and the external noise characteristics, includes: According to the following formula (11), the volume actually heard by the user is calculated: ； in, Indicates that the user is at frequency The actual volume heard under is the interference data of external noise; is the sound pressure correction value inside the auricle; the interference data of external noise Calculated by the following formula (12): ； in, Indicates the spectrum intensity of external noise; is the external noise shielding capability index; the sound pressure correction value inside the auricle Calculated by the following formula (13): ； in, The frequency of the auricle is The sound wave absorption ratio; is the frequency of the auricle The intensity of sound wave reflection; is the local curvature of the auricle surface; is the initial sound pressure value of the sound wave at the entrance of the auricle, calculated according to the following formula (14): ； in, The sound power of the mobile phone; is the gain distribution optimization parameter, which means the frequency is The gain ratio of the sound wave; It is the effective area of ​​the auricle entrance.

10. The method for adjusting personalized sound effects of a mobile phone based on auricle morphology recognition according to claim 9, characterized in that: The dynamically adjusting the audio output setting of the mobile phone based on the audio optimization parameter includes: According to the frequency response optimization parameters, the frequency distribution of the audio signal is dynamically adjusted for the specific resonance frequency band of the user's auricle, so that the signal strength is enhanced in the sensitive frequency band of the user's auricle, and the signal energy is compensated in the frequency band that is easily absorbed by the auricle; According to the gain distribution optimization parameters, combined with the sound power of the mobile phone, the output gain of each audio frequency band is dynamically adjusted, the gain is increased in the target frequency band to optimize the clarity or immersion of the audio output, and the gain of non-critical frequency bands is reduced to reduce energy waste or interference; According to the directional enhancement parameters, the signal output of the mobile phone speaker is optimized in combination with the directional characteristics of the user's auricle, so that the audio signal in the target direction is enhanced, while the sound in other directions is appropriately attenuated.

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