CN117729503A - Method for measuring auricle parameters in real time and dynamically correcting and reminding sliding of earmuffs - Google Patents
Method for measuring auricle parameters in real time and dynamically correcting and reminding sliding of earmuffs Download PDFInfo
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Abstract
The invention discloses a method for measuring auricle parameters in real time and dynamically correcting and reminding earmuff sliding. Secondly, combining with the optical photographing of a depth camera to quickly reconstruct an auricle geometric model, and quickly measuring and extracting auricle key parameters to realize the function of measuring auricle characteristic parameters in real time; finally, the auricle key parameters obtained through the contrast acousto-optic coupling detection linkage technology are used for realizing the function of sliding reminding of the earmuffs, and simultaneously, the auricle characteristic parameters are fed back to the earphone to realize the function of dynamic correction. The invention provides a new means for correcting the dislocation distortion of the earphone.
Description
Technical Field
The invention relates to the technical field of virtual auditory display, in particular to a method for measuring auricle parameters in real time and dynamically correcting and reminding sliding of an earmuff.
Background
Human hearing can perceive not only subjective properties such as intensity, pitch, timbre, etc., but also spatial properties of sound, i.e. spatial sound perception. Under certain conditions, the auditory system can localize to a target sound source in space or create a spatial impression of the surrounding acoustic environment by means of cues such as two-ear time differences, intensity differences, and phase differences. Based on this principle, spatial sound reproduction technology, also called three-dimensional audio technology, is emerging. Humans can construct virtual auditory spaces using this technique, i.e., to have listeners generate specific spatial perception information. With the development of artificial intelligence audiovisual technology, the construction of an immersive virtual auditory space gradually becomes a key component for realizing realistic virtual reality experience, and the technology has been applied to a certain extent in the fields of video entertainment, military navigation, voice communication and the like.
Spatial sound reproduction techniques can be divided into two main categories depending on the different sound reproduction devices: the first type is multi-channel loudspeaker replay, which relates to the technologies of acoustic holographic replay, spherical harmonic decomposition, wave field synthesis and the like, and mainly comprises the steps of constructing a virtual three-dimensional space environment in a specific listening area through the synergistic effect of linear array loudspeakers, so as to reconstruct an original sound field. From the practical point of view, although this method can accurately construct a sound field of a certain area, a large number of linear array speakers need to be arranged, and the configuration of the speakers is strictly required, so that the development of the technology is limited. The second type is two-channel earphone playback, mainly related to binaural pickup and virtual auditory playback, wherein the key of the binaural pickup technology is the process of picking up binaural signals by using microphones, and the key of the virtual auditory playback is a head-related transfer function.
However, the head related transfer function is largely dependent on parameters of human body characteristics related to sound reflection, diffraction and dispersion, such as head, auricle and shoulder, which are unique to each person; meanwhile, if sliding and other conditions occur in the worn double-channel earphone, virtual hearing effects presented by the earmuffs, such as front-back confusion, up-down confusion, angle deviation, head-in effect and the like, are affected, so that development of a method capable of measuring auricle characteristic parameters in real time and dynamically correcting and reminding the earmuffs to slide is urgently needed at present.
Disclosure of Invention
The invention aims at solving the technical defects existing in the prior art, and provides a method for measuring auricle parameters in real time and dynamically correcting and reminding sliding of an earmuff.
The technical scheme adopted for realizing the purpose of the invention is as follows:
a method for measuring auricle parameters in real time and dynamically correcting and reminding sliding of an earmuff comprises the following steps:
step s1, acquiring an auricle model through 3D laser scanning and CT scanning, and establishing an auricle characteristic reference database;
step s2, constructing an ear muff integral model nested with a microphone array and a depth camera, and constructing an ear muff-ear muff near-field acoustic model by combining the ear muff model obtained in the step s 1;
step s3, collecting echo signals of sound in the auricle-earmuff closed space in the auricle-earmuff near-field acoustic model, and calculating an acoustic transfer function based on a finite element simulation calculation model;
step s4, collecting auricle characteristic parameters and selecting auricle key characteristic parameters with great influence on an acoustic transfer function;
step s5, constructing an acoustic transfer function and auricle key feature parameter mapping model by using the acoustic transfer function obtained in the step s3 and the auricle key feature parameter obtained in the step s 4;
and step s6, based on the acoustic transfer function and auricle key feature parameter mapping model obtained in the step s5, acquiring echo signals reflected by the auricle by utilizing a microphone array, further predicting auricle key feature parameters, comparing the echo signals with auricle key feature parameters measured by a depth camera in real time, exceeding a auricle key feature parameter threshold value, and alarming in real time to remind the earmuff to slide and dynamically correct.
In the above technical solution, in step s1, an auricle scan image is obtained by a 3D laser scanner, so as to be used as a reference library of the mapping model of the acoustic transfer function and the auricle key feature parameters in step s5, and sample training and feature extraction are performed; the high-precision auricle model is reconstructed by acquiring auricle scanning images through a CT scanner, so that the high-precision auricle model is used as reference verification of the acoustic transfer function and the auricle key characteristic parameter mapping model in the step s 5.
In the above technical solution, in the ear muff integral model in step s2, 6 microphones are disposed at equal intervals on the inner panel of the ear muff, two adjacent microphones are spaced by 60 °, the 6 microphones form a circular microphone array, the structure of the circular microphone array is disposed in the ear muff of the headset, a set of circular microphone arrays are disposed in the left and right ear muffs of the headset, and beam forming is achieved by using time domain, frequency domain and space characteristics of the 6 different microphones.
In the above technical solution, in step s2, an auricle-earmuff near-field acoustic model is established based on three-dimensional reconstruction software, wherein the model includes auricle physiological characteristics, a microphone array, a depth camera and an earmuff.
In the above technical solution, in the step s3, an auricle-earmuff near-field acoustic model is imported into finite element software, an echo signal reflected by the auricle is obtained by setting related boundary conditions, and an acoustic transfer function is established.
In the above technical solution, in step s4, the peaman correlation analysis is adopted to screen the auricle characteristic parameters with high correlation, and then the multiple linear regression method is adopted to screen the auricle characteristic parameters with great influence on the acoustic transfer function as the auricle key characteristic parameters.
In the above technical solution, in step s5, a gaussian kernel-support vector regression mechanism is used to construct a mapping model of the acoustic transfer function and the auricle key feature parameters, the model is input as the low-dimensional feature of the acoustic transfer function, and the model is output as the auricle key feature parameters.
In the above technical solution, the dynamic correction method in step s6 is as follows: the depth camera acquires the latest parameters of auricle characteristics in real time, outputs the most reasonable auricle model, and feeds back the key auricle characteristic parameters to the personalized head related transfer function model in real time, so that the function of dynamic correction is realized.
Compared with the prior art, the invention has the beneficial effects that:
the invention can measure auricle parameters in real time based on the acoustic-optical coupling detection linkage technology, and can dynamically correct and remind the sliding of the earmuffs, thereby providing a new means for correcting the dislocation distortion of the earphone and laying an application foundation for breaking through the bottleneck of 3D auditory signal play remodulation and auditory perception technology.
Drawings
FIG. 1 is a diagram of the overall architecture of a method for measuring pinna parameters in real time while dynamically modifying the alert earmuff sliding;
FIG. 2 is a flow chart for acquiring an auricle model based on a handheld 3D laser scanner;
FIG. 3 is a flow chart for acquiring a high-accuracy auricle model based on a CT scanner;
FIG. 4 is a polar computation gain for microphone array beamforming;
FIG. 5 is an earmuff model designed based on three-dimensional reconstruction software;
FIG. 6 is a near-field acoustic model of an auricle-earmuff built based on three-dimensional reconstruction software;
fig. 7 (a) is an auricle acoustic transfer function (amplitude information) established based on finite element software;
fig. 7 (b) is an auricle acoustic transfer function (phase information) established based on finite element software.
In the figure:
1-microphone, 2-earmuff, 3-earmuff, 4-earmuff, 5-depth camera.
Detailed Description
The present invention will be described in further detail with reference to specific examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
Example 1
A method for measuring auricle parameters in real time and dynamically correcting and reminding sliding of an earmuff comprises the following steps:
step 1: establishing an auricle characteristic reference database, which comprises the following steps:
step 1.1: 100 groups of auricle models are established based on a 3D laser scanner, and the flow is shown in fig. 2, wherein the method comprises the following steps:
step 1.1.1, the 3D laser scanner is calibrated as shown in fig. 2 (a). The hand-held 3D laser scanner (thunder 3D, constant technology) is adopted, the front side is horizontally placed, the front side is inclined by 30 degrees, the rear side is inclined by 30 degrees, the left side is inclined by 30 degrees, the right side is inclined by 30 degrees, the calibration plate is shot, and the calibration of the parameters of the 3D laser scanner can be completed.
Step 1.1.2, non-contact scanning the auricle model, as shown in fig. 2 (b). Firstly, setting scanning parameters, setting object materials by adjusting brightness, setting a splicing mode as characteristic splicing, and setting a scanning mode as a small range; in the scanning process, no special requirement is made on the posture of the subject, and the subject only needs to select a comfortable posture to sit, so that noise generated by head movement is reduced, and finally, dot matrix data shown in (c) in fig. 2 are obtained.
Step 1.1.3, three-dimensional auricle model processing and reconstruction, as shown in fig. 2 (d) and (e). And (2) importing the dot matrix data obtained in the step (1.2) into Magics software for three-dimensional reconstruction, wherein the three-dimensional reconstruction comprises dot matrix synthesis and model repair (hole repair, hole detection and smoothing treatment) operations, and finally storing the dot matrix data into a stl format, as shown in (f) of fig. 2.
Step 1.2: the flow of acquiring 20 groups of high-precision auricle models based on a CT scanner is shown in fig. 3, wherein the flow comprises the following steps:
step 1.2.1, acquiring a auricle CT image, as shown in (a) of FIG. 3. The method comprises the steps of carrying out ear-nose-throat-neck surgery and imaging department assistance in hospitals such as third-level first and the like to obtain a tested auricle CT image, wherein scanning parameters are as follows: collimation is 0.325mm, rotation time is 0.4s, reconstruction layer thickness is 0.325mm, and interval is 0.25-0.5mm.
Step 1.2.2, CT image preprocessing, as shown in FIG. 3 (b). And (3) importing the DICOM data obtained in the step 1.2.1 into MIICS software to perform image preprocessing, wherein the preprocessing comprises threshold selection, region growing, image segmentation and smooth denoising, and a preprocessed auricle model is obtained.
Step 1.2.3, surface fitting and model building, as shown in fig. 3 (c). The preprocessed auricle model obtained in step 1.2.2 is imported into gemaglc software for further processing, wherein the model denoising, meshing and surface fitting operations are included, and finally the model is saved into a stl format, as shown in (d) of fig. 3.
Step 2: constructing an auricle-earmuff near-field acoustic model based on a three-dimensional reconstruction technology, wherein the model comprises auricle physiological characteristics, a loudspeaker and a microphone array and comprises the following steps:
step 2.1, designing a microphone array structure. The echo signals of the sound in the closed space are collected through the microphone array, so that the sound wave detection technology in the special environment is realized. The invention designs a circular microphone array, 6 microphones 1 are arranged on an inner panel of an earmuff at equal intervals, two adjacent microphones 1 are spaced by 60 degrees, the 6 microphones 1 form the circular microphone array, the structure of the circular microphone array is arranged in an earmuff 2 of a headset, a group of circular microphone arrays are respectively arranged in left and right earmuffs of the headset, and beam formation is realized by utilizing the time domain, frequency domain and space characteristics of 6 different microphones, so that the direction finding of the microphone arrays is achieved, as shown in fig. 4.
And 2.2, designing an earmuff integral model. Other structures of the earmuff 2 comprise an earmuff 3, an earmuff 4 and a depth camera 5, wherein the depth camera 5 is arranged at the right center of the earmuff 2, a loudspeaker is further arranged on the earmuff 2, the position of the loudspeaker can be adjusted according to requirements, and the whole structure of the earmuff 2 is shown in fig. 5.
And 2.3, constructing an auricle-earmuff near-field acoustic model. The auricle model obtained in the step 1.1, the auricle model obtained in the step 1.2 and the earmuff integral model obtained in the step 2.2 are uniformly imported into SOLIWORKS software, and the auricle model and the earmuff integral model are attached to form a closed space through coordinate movement and Boolean operation, so that an auricle-earmuff near-field acoustic model is constructed, and the model is shown in FIG. 6.
Step 3: the acoustic transfer function is calculated based on a finite element simulation calculation model, namely echo signals of sound in a closed space are collected, and the acoustic wave detection technology under special environments is realized, specifically:
and (3) importing the auricle-earmuff near-field acoustic model obtained in the step (2) into COMSOL software, and solving through a pressure acoustic-finite element module, namely establishing a finite element simulation calculation model through configuring a sound field environment, setting parameters, dividing grids and configuring a solver.
When parameters are set, surface sound source signals (100 Hz-20kHz, step length 100 Hz) with different frequencies are applied to a loudspeaker, single-frequency signals reflected by auricles are collected at the position of a microphone array, wherein the single-frequency signals comprise amplitude and phase information, and the result is shown in fig. 7 (a) and 7 (b), so that the sound wave detection technology under special environments is realized.
Step 4: the correlation analysis and the multiple linear regression method are combined to select auricle key characteristic parameters, namely auricle characteristic parameters with larger influence on an acoustic transfer function are selected, wherein the auricle key characteristic parameters comprise:
and 4.1, collecting auricle characteristic parameters. According to the human body characteristic parameter measurement standard of GB/T22187-2009, the auricle characteristic parameters of 20 tested items are measured on the basis of an auricle reference database by means of an electronic measuring tool SOLIWORKS software, and the detail is shown in Table 1.
Table 1 item 20 auricle characterization parameters
And 4.2, deleting auricle characteristic parameters with high correlation by utilizing correlation analysis. The peaman correlation analysis is adopted to screen the characteristics with high correlation degree, as shown in the formula (1):
wherein x is i And y i Is two different auricle characteristic parameters of the same tested,and->Is the average of all any two auricle characteristic parameters tested, i=1, 2 … …, M is the number of tests tested. Auricle characteristic parameters (one of which is taken by the auricle characteristic parameters) with the correlation coefficient larger than 0.8 are eliminated through the step.
Step 4.3, adopting a multiple linear regression method to further preferably select auricle characteristic parameters with great influence on acoustic transfer functions, wherein the method comprises the following steps:
(1) A regression model between the dependent and independent variables is built to further verify the significance of the independent variables. After the auricle characteristic parameters with high correlation are deleted in the step 4.2, the remaining auricle characteristic parameters are used as independent variables, and the low-dimensional characteristics of the acoustic transfer function amplitude spectrum are used as dependent variables. The multiple regression model is expressed as:
where X is the independent variable matrix, Y is the dependent variable matrix, β is the linear coefficient, ε is the residual. c is the number of low-dimensional features and e is the number of auricle feature parameters.
(2) The T-test is used to determine which auricle characteristic parameters have a significant effect on the amplitude spectrum of the acoustic transfer function, and finally, 5 key auricle characteristic parameters are preferred, namely, the inner auricle width, the triangular fossa height, the concha cavity width and the inter-tragus width are used as auricle key characteristic parameters.
Step 5: constructing an acoustic transfer function and auricle key characteristic parameter mapping model based on support vector regression, wherein the method comprises the following steps of:
and 5.1, selecting a training set and a testing set. The acoustic transfer function and the auricle key characteristic parameters calculated based on 100 groups of auricle reference databases acquired by a 3D laser scanner are used as training sets, and the acoustic transfer function and the auricle key characteristic parameters calculated based on 20 groups of high-precision auricle images acquired by a CT scanner are used as verification sets.
And 5.2, preprocessing data. In order to prevent the excessive or insufficient value of some auricle key characteristic parameter in the sample from causing the reduction of training efficiency of the model, all auricle key characteristic parameters in the sample are normalized, wherein the parameter range is [ -1,1].
And 5.3, optimizing model parameters. The Gaussian radial basis function is used as a kernel function of a support vector regression model, an optimal super-parameter (punishment function, kernel function coefficient and loss measurement) is selected by a ten-fold cross validation method, and the three super-parameter value ranges of the punishment function, the kernel function coefficient and the loss distance measurement are set through a grid search method.
And 5.4, training a model. Training the support vector regression model based on the optimal super parameters obtained in the step 5.3, testing the test set data based on the model after training, and evaluating the prediction performance of the established mapping model through the generalization error index of the model.
Step 6: sounding-optical coupling detection linkage technology based on microphone array and depth camera, comprising:
and 6.1, performing rapid geometric reconstruction on the auricle by utilizing optical photographing of a depth camera, and measuring and extracting key characteristic parameters of the auricle in real time.
And 6.2, measuring auricle key characteristic parameters by using a microphone array based on the acoustic transfer function and auricle key characteristic parameter mapping model, comparing the auricle key characteristic parameters measured by the depth camera in the step 6.1 with the auricle key characteristic parameters, and realizing the function of real-time alarming and reminding of the sliding of the earmuff by setting the auricle key characteristic parameter threshold.
And 6.3, acquiring the latest feature parameters of auricle features in real time by optical photographing of the depth camera, outputting the most reasonable auricle model, and feeding back the auricle model to a personalized head-related transfer function model in the earphone (the personalized head-related transfer function model is constructed in the prior art and is not repeated here), so as to realize the function of dynamic correction.
The foregoing is merely a preferred embodiment of the present invention and it should be noted that modifications and adaptations to those skilled in the art may be made without departing from the principles of the present invention, which are intended to be comprehended within the scope of the present invention.
Claims (7)
1. The method for measuring auricle parameters in real time and dynamically correcting and reminding the sliding of the earmuff is characterized by comprising the following steps of:
step s1, acquiring an auricle model through 3D laser scanning and CT scanning, and establishing an auricle characteristic reference database;
step s2, constructing an ear muff integral model nested with a microphone array and a depth camera, and constructing an ear muff-ear muff near-field acoustic model by combining the ear muff model obtained in the step s 1;
step s3, collecting echo signals of sound in the auricle-earmuff closed space in the auricle-earmuff near-field acoustic model, and calculating an acoustic transfer function based on a finite element simulation calculation model;
step s4, collecting auricle characteristic parameters and selecting auricle key characteristic parameters with great influence on an acoustic transfer function;
step s5, constructing an acoustic transfer function and auricle key feature parameter mapping model by using the acoustic transfer function obtained in the step s3 and the auricle key feature parameter obtained in the step s 4;
and step s6, based on the acoustic transfer function and auricle key feature parameter mapping model obtained in the step s5, acquiring echo signals reflected by the auricle by utilizing a microphone array, further predicting auricle key feature parameters, comparing the echo signals with auricle key feature parameters measured by a depth camera in real time, exceeding a auricle key feature parameter threshold value, and alarming in real time to remind the earmuff to slide and dynamically correct.
2. The method for measuring auricle parameters in real time and dynamically correcting and reminding the sliding of the earmuff according to claim 1, wherein in the step s1, an auricle scanning image is obtained through a 3D laser scanner to be used as a reference library of the mapping model of the acoustic transfer function and the auricle key characteristic parameters in the step s5 for sample training and characteristic extraction; the high-precision auricle model is reconstructed by acquiring auricle scanning images through a CT scanner, so that the high-precision auricle model is used as reference verification of the acoustic transfer function and the auricle key characteristic parameter mapping model in the step s 5.
3. The method for measuring auricle parameters in real time and dynamically correcting and reminding sliding of an earmuff according to claim 1, wherein in the earmuff integral model in the step s2, 6 microphones are arranged on an inner panel of the earmuff at equal intervals, two adjacent microphones are spaced by 60 degrees, the 6 microphones form a circular microphone array, the structure of the circular microphone array is arranged in an earmuff of a headset, a group of circular microphone arrays are respectively arranged in left and right earmuffs of the headset, beam formation is achieved by utilizing time domain, frequency domain and space characteristics of 6 different microphones, a depth camera is arranged in the center of the earmuff, and a loudspeaker is further arranged on the earmuff.
4. The method for measuring auricle parameters in real time and dynamically correcting and reminding the sliding of an earmuff according to claim 1, wherein in the step s3, an auricle-earmuff near-field acoustic model is imported into finite element software, echo signals reflected by the auricle are obtained by setting related boundary conditions, and an acoustic transfer function is established.
5. The method for measuring auricle parameters in real time and dynamically correcting and reminding the sliding of the auricle according to claim 1, wherein in the step s4, the auricle characteristic parameters with high correlation degree are screened out by adopting a Pearman correlation analysis, and then the auricle characteristic parameters with great influence on an acoustic transfer function are screened out by adopting a multiple linear regression method to serve as auricle key characteristic parameters.
6. The method for measuring auricle parameters in real time and dynamically correcting and reminding auricle sliding according to claim 1, wherein in step s5, a gaussian kernel-support vector regression mechanism is used for constructing an acoustic transfer function and auricle key feature parameter mapping model, the model is input as a low-dimensional feature of the acoustic transfer function, and the model is output as auricle key feature parameters.
7. A method for measuring auricle parameters in real time and dynamically correcting and reminding sliding of an earmuff according to claim 1, wherein the dynamic correction method in step s6 is as follows: the depth camera acquires the latest parameters of auricle characteristics in real time, outputs the most reasonable auricle model, and feeds back the key auricle characteristic parameters to the personalized head related transfer function model in real time, so that the function of dynamic correction is realized.
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