CN115444381A - In-vehicle sound quality evaluation method and system based on physiological signals - Google Patents
In-vehicle sound quality evaluation method and system based on physiological signals Download PDFInfo
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Abstract
The invention discloses a method and a system for evaluating the quality of sound in a vehicle based on physiological signals, wherein the evaluation method comprises the following steps: step one, collecting heart rate signals, pulse signals and respiratory signals of a testee under a plurality of running conditions; obtaining a plurality of physiological signal characteristic parameters under different driving conditions according to the heart rate signal, the pulse signal and the respiration signal; determining objective psychological comprehensive evaluation index values under the multiple driving conditions; step two, respectively calculating the association degree of each physiological signal characteristic parameter and the objective psychological comprehensive evaluation index value, and obtaining the weight of the physiological signal characteristic parameter according to the association degree; step three, calculating a physiological comprehensive evaluation index I s ：
Wherein m represents the number of physiological characteristic parameters, W i Weight, X, representing characteristic parameter of the ith physiological signal i "denotes the normalized value of the characteristic parameter of the i-th physiological signal.
Description
Technical Field
The invention belongs to the technical field of interior sound quality evaluation, and particularly relates to an interior sound quality evaluation method and system based on physiological signals.
Background
The sound quality research is a research field with multiple interdisciplines, and mainly relates to the subjects of acoustics, physics, psychology and the like. In actual research, sound quality evaluation can usually be started from both objective evaluation and subjective evaluation.
The subjective evaluation of acoustic quality may be deployed in questionnaires or subjective evaluation tests. The method comprises the following steps that a reviewer completes description on subjective perception characteristics of sound by using pre-selected professional terms, and the purpose of sound quality evaluation is achieved; the objective evaluation of sound quality evaluates the quality of sound by measuring and calculating physical parameters of sound and psycho-acoustic objective parameters representing the psychological change of the sound caused by human. In the two types of evaluation methods, a large amount of manpower and material resources are required to be consumed in a subjective evaluation test, and adverse factors such as difficulty in obtaining a large amount of evaluation data, unstable evaluation results, high randomness and the like exist; the objective evaluation is completed by performing objective index calculation on the collected sound samples, the result is stable, the operation is simple, and the defects of subjective evaluation can be made up to a certain extent. However, objective evaluation only calculates a specific number of parameter values for a certain target sound environment, and cannot reflect the sound quality perception difference of different individuals in the sound environment.
For example, subjective evaluation in a certain vehicle interior sound environment has the problems of time and labor consumption, limited data volume, unstable evaluation result, high randomness and the like in the evaluation; if objective evaluation is adopted, the result is a single fixed numerical value for a certain physical parameter or psychoacoustic objective parameter.
For the limitations of the above two methods, physiological studies show that human sensory experience is regulated by the central nervous system and the autonomic nervous system together, and physiological signals can show changes as a natural response of regulation, and the response and changes are not controlled by the subjective will of human beings. Considering the application scene in the car, the feeling of the driver and the passengers on the sound quality in the car can influence the corresponding physiological response to a certain extent. The quality of the sound in the vehicle is evaluated by utilizing the physiological signals, so that the evaluation result is more objective and real, and the individual difference can be fully reflected. And the physiological signal has better robustness, objectivity and continuous testability under higher time precision, so that the physiological signal analysis and application for the human body are widely concerned in various fields.
Disclosure of Invention
An object of the present invention is to provide an in-vehicle sound quality evaluation system based on a physiological signal, which is capable of acquiring a physiological signal of a subject and performing in-vehicle sound quality evaluation according to a psychological signal of the subject.
The invention also aims to provide a method for evaluating the sound quality in the vehicle based on the physiological signals, which adopts the relatively mature and clear physiological signals with research significance, such as heart rate signals, pulse signals, respiratory signals and the like, and determines the physiological comprehensive evaluation index of the sound quality in the vehicle by taking objective psychological evaluation of the sound quality as a reference, thereby obtaining a real and objective evaluation result.
The technical scheme provided by the invention is as follows:
an in-vehicle sound quality evaluation system based on physiological signals, comprising:
a heart rate sensor for detecting a subject heart rate signal;
a pulse sensor for detecting a subject pulse signal;
a respiration sensor for detecting a respiration signal of the subject;
a disconnection test box electrically connected to the rate sensor, the pulse sensor, and the respiration sensor, respectively;
the automatic box is connected with the disconnection testing box through a wire harness;
and the upper computer is connected with an upper computer interface of the Autobox and used for evaluating the quality of the internal sound of the vehicle according to the acquired data.
Preferably, the system for evaluating the sound quality in the vehicle based on the physiological signal further includes: and the noise storage and playing device is used for storing and playing the noise.
A method for evaluating the quality of sound in a vehicle based on physiological signals comprises the following steps:
step one, collecting heart rate signals, pulse signals and respiratory signals of a testee under a plurality of running working conditions; obtaining a plurality of physiological signal characteristic parameters under different driving conditions according to the heart rate signal, the pulse signal and the respiration signal; determining objective psychological comprehensive evaluation index values under the multiple driving conditions;
step two, respectively calculating the association degree of each physiological signal characteristic parameter and the objective psychological comprehensive evaluation index value, and obtaining the weight of the physiological signal characteristic parameter according to the association degree;
step three, calculating a physiological comprehensive evaluation index I s ：
Wherein m represents the number of physiological characteristic parameters, W i Weight, X' representing characteristic parameter of ith physiological signal i Representing the normalized value of the characteristic parameter of the ith physiological signal.
Preferably, the driving condition includes: the vehicle was operated at idle, vehicle speed 10km/h, vehicle speed 20km/h, vehicle speed 30km/h, vehicle speed 40km/h, vehicle speed 50km/h, vehicle speed 60km/h and vehicle speed 70 km/h.
Preferably, the physiological signal characteristic parameters include: average RR interval, RR interval standard deviation, heart rate low-frequency power, heart rate high-frequency power, heart rate low-frequency power and high-frequency power ratio, pulse adjacent main wave interval mean value, pulse adjacent main wave interval standard deviation, pulse main wave peak value mean value, pulse main wave peak value standard deviation, the percentage of pulse main wave interval more than 50ms, respiration adjacent wave interval mean value, respiration adjacent wave interval standard deviation, respiration adjacent wave interval first-order difference mean value, respiration adjacent wave interval first-order difference standard deviation and respiration adjacent wave interval second-order difference mean value.
Preferably, the objective psychological comprehensive evaluation index value is calculated by a method comprising:
I p ＝-0.2L′+0.2F′+0.2R′+0.2S′+0.2AI′；
wherein, I p For objective psychology comprehensive evaluation index value, L ' represents normalized value of loudness, F ' represents normalized value of jitter, R ' represents normalized value of roughness, S ' represents normalized value of sharpness, and AI ' represents normalized value of language definition.
Preferably, in the second step, the calculating a degree of association between each physiological signal characteristic parameter and the objective psychological overall evaluation index value includes the following steps:
step 1, processing objective psychological comprehensive evaluation index values and physiological signal characteristic parameters to respectively obtain initial value images of the objective psychological comprehensive evaluation index values and the physiological signal characteristic parameters;
step 2, calculating a correlation coefficient between the objective psychological comprehensive evaluation index value and the physiological signal characteristic parameter:
in the formula, gamma 0i (k) The correlation coefficient represents the i-th physiological signal characteristic parameter and the objective psychological comprehensive evaluation index value under the k-th driving condition; I.C. A p ' (k) an initial value image showing objective psychometric integration evaluation index values under the k-th driving condition; x is a radical of a fluorine atom i ' (k) represents an initial value image of the characteristic parameter of the ith physiological signal under the kth driving condition, xi is a resolution coefficient, and xi belongs to (0, 1);
step 3, calculating the association degree of the physiological signal characteristic parameters and the objective psychological comprehensive evaluation index value:
in the formula, n is the number of running conditions; r is 0i And representing the degree of association between the ith physiological signal characteristic parameter and the objective psychological comprehensive evaluation index value.
Preferably, in the second step, the obtaining the weight of the physiological signal characteristic parameter includes:
step a, calculating a weight absolute value of the physiological signal characteristic parameter:
in the formula, W i Represents the weighted absolute value of the ith physiological signal characteristic parameter,
b, determining the sign of the physiological signal characteristic parameter by adopting the following method:
and calculating the covariance of the objective psychological comprehensive evaluation index value and each physiological signal characteristic parameter, wherein if the covariance is a positive value, the weight sign of the physiological signal characteristic parameter is positive, otherwise, the weight sign of the physiological signal characteristic parameter is negative.
Preferably, the resolution coefficient ξ =0.5.
The invention has the beneficial effects that:
the in-vehicle sound quality evaluation system based on the physiological signals can acquire the physiological signals of the testee and evaluate the in-vehicle sound quality according to the physiological signals of the testee.
The in-vehicle sound quality evaluation system based on the physiological signals provided by the invention builds the acquisition platform by taking the Autobox as the core, avoids the complex work in the building process of the core platform of the single chip microcomputer, and is convenient for flexible adjustment of the system. The system is a structure consisting of a physiological signal acquisition device and a noise storage and playing device, can be elastically arranged on a real vehicle and a simulated driver, and can be more possibly adapted to the limits of experimental conditions and safety requirements.
According to the method for evaluating the sound quality in the vehicle based on the physiological signals, disclosed by the invention, the physiological signals which are relatively mature and definite and have research significance are analyzed by using the heart rate signals, the pulse signals, the respiratory signals and the like, and the physiological comprehensive evaluation index of the sound quality in the vehicle is determined by taking objective psychological evaluation of the sound quality as a reference, so that a real and objective evaluation result can be obtained, and the individual differences of different drivers and passengers can be fully reflected. The sound quality evaluation based on the physiological signals guides the interior sound environment of the electric vehicle to be actively constructed, and the interior sound environment is actively adjusted in real time according to the physiological index levels of different users, so that the personalized and intelligent customization of the interior sound quality can be realized, better driving experience is provided for the users, and the vehicle brand competitiveness is further improved.
Drawings
Fig. 1 is a schematic structural diagram of a physiological signal acquisition device according to the present invention.
Fig. 2 is a flowchart of the method for evaluating the interior acoustic quality based on physiological signals according to the present invention.
Fig. 3 is a layout diagram of a noise storage and playback apparatus according to the present invention.
Detailed Description
The present invention is further described in detail below with reference to the attached drawings so that those skilled in the art can implement the invention by referring to the description text.
The invention provides a method and a system for evaluating sound quality in a vehicle based on physiological signals. The in-vehicle sound quality evaluation system based on the physiological signals is composed of a physiological signal acquisition device and a noise storage and playing device. The basic part of the evaluation system is a physiological signal acquisition device which is arranged on an experimental vehicle, so that the in-vehicle sound quality evaluation based on the physiological signal can be completed through a road test; when the device is limited by experimental conditions or in consideration of safety, a noise storage and playing device can be additionally arranged on the basis of the physiological signal acquisition device and arranged on the simulated driver, and the evaluation of the sound quality in the vehicle based on the physiological signal can also be completed.
The physiological signal acquisition device takes an Autobox as a core, the Autobox is electrically connected with a disconnection test box through a wire harness, the disconnection test box is electrically connected with a heart rate sensor, a pulse sensor and a respiration sensor, an upper computer interface Host-PC of the Autobox is electrically connected with a USB interface of a notebook computer through a network cable, and a model is built and codes are compiled on the notebook computer by utilizing a Simulink assembly of MATLAB.
As shown in fig. 1, in an embodiment, the physiological signal acquisition device uses an Autobox as a core, the Autobox is electrically connected with a disconnection test box through a wire harness, pins M1, c4, c5 and c6 of the disconnection test box are electrically connected with pins 5V, GND, rx and Tx of an RS232 serial port of a heart rate sensor respectively, pins M1, B4, B5 and B6 are electrically connected with pins 5V, GND, rx and Tx of an RS232 serial port of a pulse sensor respectively, pins M1, P4, P5 and P6 are electrically connected with pins 5V, GND, rx and Tx of an RS232 serial port of a respiration sensor respectively, an upper computer interface Host-PC of the Autobox is electrically connected with a USB interface of a notebook computer through a network cable, a model is built on the notebook computer by using a Simulink component of MATLAB, and codes are compiled.
The basic technical index requirements of various physiological signal sensors are as follows: the heart rate is above 200Hz, the pulse is above 200Hz, and the breath is above 50 Hz. Based on the above requirements, in one embodiment, a product of the institute of electronics and technology in Hefei Huake is selected, and the specific parameters are shown in Table 1:
TABLE 1 physiological signal sensor parameter table
In one embodiment, the storage device selected by the noise storage and playback apparatus is MP3, the german seassel HD650 high-performance fidelity headphone is selected by the playback device, and the Aux interface of the MP3 is electrically connected to the headphone through a 3.5mm small three-core connecting wire.
The evaluation system takes an Autobox as a core to build an acquisition platform, so that the complex work in the building process of the core platform of the single chip microcomputer is avoided, and the system can be flexibly adjusted conveniently. The system is a structure consisting of a physiological signal acquisition device and a noise storage and playing device, can be elastically arranged on a real vehicle and a simulated driver, and can be more possibly adapted to the limits of experimental conditions and safety requirements.
In consideration of the evaluation comprehensiveness, five parameters of loudness, jitter degree, roughness, sharpness and language definition in objective psychoacoustic parameters commonly adopted by researchers in the field of the quality of the sound in the vehicle at present are selected, and the parameters are distributed in equal weight to form an objective psychoacoustic comprehensive evaluation index value. And establishing a grey correlation analysis model by using the grey correlation analysis theory according to the obtained physiological signal characteristic parameters by taking the objective psychological comprehensive evaluation index value as a reference, obtaining physiological signal characteristic parameter sequencing suitable for the in-vehicle sound quality evaluation according to the correlation degree with the objective comprehensive evaluation index, obtaining an in-vehicle sound quality physiological comprehensive evaluation index according to the grey correlation degree value and the polarity relation, and establishing the in-vehicle sound quality evaluation method based on the physiological signal.
As shown in fig. 2, the evaluation method is implemented as follows:
1. data acquisition
Designing a running condition:
in one embodiment, 8 driving conditions are set. The first working condition, the second working condition, the third working condition, the fourth working condition, the fifth working condition, the sixth working condition, the seventh working condition and the eighth working condition are defined as the running working conditions of the selected vehicle under the conditions of idle speed, 10km/h, 20km/h, 30km/h, 40km/h, 50km/h, 60km/h and 70km/h respectively.
(1) Noise signal collecting and recording and objective psychoacoustic parameter calculation in real vehicle
a. Collecting and recording noise signals in the vehicle: and the real vehicle carries out the in-vehicle noise collection and recording under the eight working conditions. The test conditions of the acquisition and recording environment requirements are acoustic conditions, meteorological conditions and test road conditions which accord with GB/T18697-2002 Acoustic-automobile in-vehicle noise measurement method:
a1. the sound radiated from the automobile is required to become a part of the noise inside the automobile only by reflection from the road surface, but not by reflection from a building, a wall, or a similar large object outside the automobile; therefore, the distance between the car and such large objects should be more than 20 meters during the measurement;
a2. the temperature outside the car must be between-5 ℃ and 35 ℃ and the wind speed must not exceed 5 meters per second at a height of about 1.2 meters along the measuring route;
a3. the road section to be tested should be a hard road surface, which must be as smooth as possible without joints, bumps or similar surface structures, otherwise the sound pressure level inside the vehicle would be increased; the road surface must be dry and must not have snow, dirt, stones, leaves and other impurities.
In order to collect and play back more real sound, the simulation artificial head and a multi-channel testing system matched with the simulation artificial head are used as noise recording equipment, and the noise sound pressure levels of the left ear and the right ear of the simulation artificial head under different sound pressure levels are recorded.
In one embodiment, the noise recording device employs a B & K company 4100D simulated dummy head, 3560B multichannel test system, connected using dedicated cables.
b. Objective psychoacoustic parameter calculation
Considering the evaluation comprehensiveness, five parameters of loudness, jitter degree, roughness, sharpness and language definition in objective psychoacoustic parameters commonly adopted by researchers in the field of sound quality in the vehicle at present are selected, the parameters are distributed in equal weight, and the relevant polarity is determined by referring to the research experience of predecessors, so that an objective psychoacoustic comprehensive evaluation index is formed.
And calculating five objective psychoacoustic parameters of loudness, jitter degree, roughness, sharpness and speech definition by using acoustic processing software according to the collected in-vehicle noise signal.
To achieve the above purpose, software calculation is needed. In one embodiment, the LMS Test Lab noise vibration analysis software was used to obtain five objective psychoacoustic parameters. The LMS Test Lab noise vibration analysis software is a whole set of LMS vibration measurement system, is the combination of high-speed multi-channel data acquisition and Test, analysis and electronic report tools, and has the functions of data acquisition, digital signal processing, structural Test and the like. Five objective psychoacoustic parameters of loudness, jitter, roughness, sharpness and language definition are calculated by using a Sound Quality Metrics module in LMS Test Lab software according to the collected in-vehicle noise signals.
In one embodiment, each objective psychoacoustic parameter (loudness, jitter, roughness, sharpness and speech clarity) is dimensionless, and a normalization value of each parameter is obtained by averaging;
wherein Y is i ' is normalized Objective psychoacoustic parameter, Y i In order to normalize the objective psychoacoustic parameters prior to normalization,
is the average of the corresponding objective psychoacoustic parameters.
And then, carrying out equal weight distribution on each parameter according to the experience and the importance of the parameter to form an objective psychological comprehensive evaluation index value.
I p ＝-0.2L′+0.2F′+0.2R′+0.2S′+0.2AI′；
Wherein, I p For objective psychology comprehensive evaluation index values, L ' represents a normalized value of loudness, F ' represents a normalized value of jitter, R ' represents a normalized value of roughness, S ' represents a normalized value of sharpness, and AI ' represents a normalized value of speech intelligibility.
(2) The physiological signal acquisition device is laid and debugged: the physiological signal acquisition device is reasonably arranged on a tested vehicle or a simulated driver, as shown in figure 1. On the premise of not interfering with driving behaviors, the measuring end of the heart rate sensor is placed in front of the chest of a testee, the measuring end of the pulse sensor is placed at the fingertip of the testee, and the measuring end of the respiration sensor is fixed around the waist of the testee, as shown in fig. 1. And debugging the device, and formally starting to collect signals by confirming that the signals of the notebook terminal normally receive the signals and recording data.
(3) Real vehicle driving physiological signal acquisition experiment:
the experimental conditions are as follows: the experimental environment is outdoor, the road surface is dry and smooth without seams, large objects are not arranged in 20 meters around, the experimental environment is far away from interference sources such as other electric equipment, the environmental temperature is about-5 ℃ to 35 ℃, and the air is dry.
The tested vehicle completes the driving tasks under the eight working conditions as required. In the driving process under each working condition, the physiological signal acquisition device measures the heart rate, the pulse and the respiratory signal of a driver in real time. The duration of each working condition and each measurement is 5 minutes, and all the acquired physiological signal data are stored in the notebook computer by the physiological signal acquisition device. When the experiment condition allows, the real vehicle driving physiological signal acquisition experiment can be completed together with the in-vehicle noise acquisition and recording experiment.
(4) The simulation driving physiological signal acquisition experiment comprises the following steps:
the experimental conditions are as follows: the experimental environment is indoor, the surrounding of the simulated driver is quiet, the simulated driver is far away from other interference sources such as electric equipment, the experimental environment has the advantages of good illumination and ventilation, the room temperature is about 25 ℃, and the air is dry.
a. Playback of noise in the vehicle: on the simulated driver, the model same as the actual experimental vehicle is selected, and the road environment consistent with the typical working condition noise in the vehicle is collected and recorded, and the simulated driving under the eight working conditions is completed on the simulated driver in sequence by a test. And in the driving process under each working condition, the noise storage and playing device is utilized to synchronously play the corresponding noise in the vehicle according to the working condition. And in the driving process under each working condition, the noise storage and playing device is utilized to synchronously play the corresponding noise in the vehicle according to the working condition. The noise storage and playing device consists of a storage device MP3 and a playing device German Seisassel HD650 high-performance fidelity earphone, wherein an Aux interface of the MP3 is electrically connected with the earphone through a 3.5mm small three-core connecting wire. The specific arrangement position is shown in fig. 3.
b. And (3) physiological signal acquisition, namely measuring the heart rate, the pulse and the respiratory signal of the driver in real time by a physiological signal acquisition device, wherein the measuring time of each working condition is 5 minutes, and all acquired physiological signal data are stored in a notebook computer by the physiological signal acquisition device.
2. Physiological signal data processing:
and processing the acquired physiological signal data and calculating characteristic parameters. Preferably, in this embodiment, 15 characteristic parameters commonly used in the correlation study of the heart rate, pulse and respiratory physiological signals are selected, and the calculation process of each physiological signal parameter is shown in table 2:
TABLE 2 physiological signal parameter calculation Process
3. Method for evaluating sound quality based on physiological signal and model establishment
And establishing a gray correlation model for the selected 15 physiological parameters by taking the objective psychology comprehensive evaluation index value of the sound quality in the vehicle as a reference.
The grey correlation analysis theory is an important component of the grey system theory. The basic theoretical idea is to judge the closeness of association of different data sequences according to the similarity between the geometric shapes of the analyzed data. The method utilizes a linear interpolation method to convert the discrete behavior observed value of the system factor into a piecewise continuous broken line, and further constructs a model of the association degree according to the geometric characteristics of the broken line. The closer the polyline geometry, the greater the degree of association between the respective sequences.
In one embodiment, a sequence formed by the objective psychometric comprehensive evaluation index values of the sound quality in the vehicle under the 8 working conditions is used as a reference sequence reflecting the behavior characteristics of the system. A sequence consisting of physiological characteristic parameter values under 8 working conditions is used as a comparison sequence influencing the system behavior, and 15 comparison sequences are used in total. And taking a sequence consisting of the objective comprehensive evaluation index values of the sound quality in the vehicle under 8 working conditions as a reference sequence reflecting the behavior characteristics of the system. Grey correlation calculations and ordering between the comparison sequences and the reference sequence are performed. And carrying out weight distribution on each physiological signal characteristic parameter according to the relevance ranking. The specific implementation process is as follows:
a sequence consisting of physiological characteristic parameter values under 8 working conditions is used as a comparison sequence influencing the system behavior, and 15 comparison sequences are used in total. And taking a sequence consisting of the objective psychology comprehensive evaluation index values of the sound quality in the vehicle under 8 working conditions as a reference sequence reflecting the behavior characteristics of the system. Gray correlation calculation and ranking between the comparison sequence and the reference sequence are performed.
The 15 physiological signal characteristic parameters used:
MEANh, SDNNh, LF, HF, LF/HF, means 1, stdp1, means 2, stdp2, K, means 1, stdb1, delta, theta, gamma, respectively.
(1) Determination of analytical sequences
And dividing the system behavior sequence, and determining a reference sequence reflecting the system behavior characteristics and a comparison sequence influencing the system behavior.
The reference sequence (also called parent sequence) is X 0 ＝X 0 (k) K =1,2, \8230;, n; where X is 0 (k)＝I p (k) Expressing objective psychological comprehensive evaluation index values under the working conditions in the kth;
comparing sequences (also known as subsequences) to X i ＝X i (k),k＝1,2,…,n,i＝1,2,…m；X i Representing the characteristic parameter of the ith physiological signal;
in this embodiment, n is the number of selected acoustic environments (the number of driving conditions), 8 conditions correspond to 8 acoustic environments, and n is 8.m is the number of the selected physiological signal characteristic parameters and has a value of 15.
(2) Obtaining an initial image of the variables
The variables (objective psychological comprehensive evaluation index value and physiological signal characteristic parameter) are processed as follows by selecting initialization processing, and an initial value image of each sequence is obtained.
X′ 0 ＝X 0 /x 0 (1)＝(x′ 0 (1),x′ 0 (2),...,x′ 0 (k))
X′ i ＝X i /x i (1)＝(x′ i (1),x′ i (2),...,x′ i (k)),i＝1,2,…,m；
(3) Calculating the correlation coefficient
Finding X 0 (corresponding to ObjectiveIndex value I for comprehensive psychological evaluation p Sequence) and X i Absolute value sequence of the difference between the corresponding components of the initial value image (physiological signal characteristic parameter sequence):
Δ i (k)＝|x′ 0 (k)-x′ i (k)|,Δ i ＝(Δ i (1),Δ i (2),…,Δ i (n)),i＝1,2,…,m；
calculating Delta i (k)＝|x′ 0 (k)-x′ i (k) L, k =1,2, \8230;, n; i =1,2, \8230;, maximum and minimum values of m. Respectively recorded as:
calculating a correlation coefficient:
x 'here' 0 (k)＝I p ' (k); then
The smaller xi is, the larger the resolution is, and the specific value can be determined according to the situation. When ξ is not more than 0.5463, the resolution is the best, and ξ =0.5 is preferable.
(4) Calculating the degree of association
The average value of the correlation coefficients, i.e., the desired degree of correlation, is obtained.
(5) Rank of relevance
And obtaining corresponding sequencing according to the correlation degree of all the physiological signal characteristic parameters and the objective comprehensive evaluation indexes.
(6) Determination of weight absolute value of physiological comprehensive evaluation index of sound quality in vehicle
In order to establish the physiological comprehensive evaluation index of the sound quality in the vehicle based on the physiological signal, the weight absolute value is determined according to the relevance degree sequence between the physiological signal characteristic parameter analyzed in the front and the objective comprehensive evaluation index. And normalizing the grey correlation degree of each physiological signal characteristic parameter and the objective comprehensive evaluation index.
Using the normalization formula:
in the formula, W i Representing the absolute value of the weight of the characteristic parameter of the ith physiological signal, r 0i And representing the degree of association between the ith physiological signal characteristic parameter and the objective psychological comprehensive evaluation index value.
On the basis, the covariance of the objective comprehensive evaluation index and each physiological signal characteristic parameter is respectively calculated to determine the relevant polarity between the objective comprehensive evaluation index and each physiological characteristic parameter, and further determine the sign of each weight. If the covariance is positive value, that is, the variation trend of the covariance is consistent with that of the physiological signal, the weight corresponding to the physiological signal parameter is set to be positive value. If the covariance is negative, i.e. the two change trends are opposite, the weight corresponding to the physiological signal parameter is set to be negative.
Wherein, the covariance calculation formula is:
COV(X 0 ,X i )＝E[(X 0 -E(X 0 ))(X i -E(X i ))],i＝1,2,…,m
(7) Establishing comprehensive physiological indexes
And respectively calculating the covariance of the objective comprehensive evaluation index and each physiological signal characteristic parameter to determine the relevant polarity between the objective comprehensive evaluation index and each physiological characteristic parameter, further determining the sign of each weight, and establishing the physiological comprehensive evaluation index.
Carrying out dimensionless treatment on the characteristic parameters of each physiological signal, and obtaining the normalized value of each parameter by adopting averaging treatment;
wherein, X ″) i To be a normalized value of a characteristic parameter of the physiological signal,
is the average value of the corresponding physiological signal characteristic parameter.
Finally, the physiological comprehensive evaluation indexes are established as follows:
wherein m represents the number of physiological characteristic parameters, W i Weight, X ″, representing the characteristic parameter of the ith physiological signal i Representing the normalized value of the characteristic parameter of the ith physiological signal.
The same driver is tested, and the physiological comprehensive evaluation index I is obtained under different sound environments s Different values of (a). Wherein, I s The larger the sound environment, the higher the acceptability of the sound environment, i.e. the higher the acceptable level of the sound environment for the driver.
The sound quality physiological comprehensive evaluation index can be used for guiding the active construction of the sound environment in the electric vehicle. Specifically, the following two cases are included:
in the forward development of electric vehicles, different sound environments can be formed by using different interior materials, in-vehicle acoustic structures and active sound production modes. The method can select a certain typical working condition (such as a specified vehicle type A, running on a road surface conforming to GB/T18697-2002 at a speed of 40 km/h) of a certain pair of standard vehicles to obtain an interior acoustic environment, measure physiological signals of a driver and passenger group through an acoustic quality physiological comprehensive evaluation system based on a road test or a simulated driving experiment aiming at a product target group, and carry out an acoustic quality evaluation test by utilizing the physiological evaluation comprehensive indexes to obtain I s Is used as the group sound quality physiological comprehensive evaluation index reference I ss . Under the test car interior sound environment with the same working condition, the same driver and passenger group measures and calculates to obtain I s Average value of (1), i.e. physiological overall evaluation index I of test vehicle st . If I st Value greater than I ss Then, the sound environment of the test vehicle is better than the sound environment of the standard vehicle; otherwise, the sound environment of the test vehicle is worse than that of the target vehicle. And changing conditions such as road conditions, vehicle speed and the like, and then guiding the optimization construction of the sound environment in the vehicle by contrasting the benchmarking vehicle.
When the electric automobile is used daily, the in-automobile active sound system is responsible for constructing and generating in-automobile sound environments of different types, physiological signals of current specific drivers and passengers can be measured in real time by utilizing the equipped physiological signal acquisition device, and the physiological evaluation comprehensive index I is calculated through a model s As a physiological comprehensive evaluation index standard I for specific human voice quality ss And the information is correspondingly stored in groups with the type of the sound environment in the vehicle, even the information such as the working condition, the time, the weather and the like of the vehicle. If under the same vehicle working condition, time and weather, a new I corresponding to the type of the sound environment in the vehicle exists s If the value is higher, the update can be marked, and the new benchmark I is stored in groups ss And a corresponding in-vehicle sound environment category. Namely, the reference I can be used as a basis to call the reference I in real time according to the running condition of the vehicle ss And the corresponding type of the sound environment in the vehicle, the active sounding mode is adjusted, the sound environment in the vehicle is continuously optimized, the personalized sound space design is realized, and better driving experience is provided.
In conclusion, the sound quality evaluation method based on the physiological signals can obtain physiological evaluation comprehensive indexes through measurement of the physiological signals of drivers and passengers and model calculation, effectively supplements and expands the currently adopted in-vehicle sound quality evaluation method, guides personalized and intelligent customization of in-vehicle sound quality, and improves the competitiveness of vehicle brands.
While embodiments of the invention have been described above, it is not limited to the applications set forth in the description and the embodiments, which are fully applicable in various fields of endeavor to which the invention pertains, and further modifications may readily be made by those skilled in the art, it being understood that the invention is not limited to the details shown and described herein without departing from the general concept defined by the appended claims and their equivalents.
Claims (9)
1. An in-vehicle sound quality evaluation system based on a physiological signal, comprising:
a heart rate sensor for detecting a subject heart rate signal;
a pulse sensor for detecting a subject pulse signal;
a respiration sensor for detecting a respiration signal of the subject;
a disconnection test box which is respectively electrically connected with the rate sensor, the pulse sensor and the respiration sensor;
the automatic box is connected with the disconnection testing box through a wire harness;
and the upper computer is connected with an upper computer interface of the Autobox and used for evaluating the internal sound quality of the vehicle according to the acquired data.
2. The physiological signal-based in-vehicle sound quality evaluation system according to claim 1, further comprising: and the noise storage and playing device is used for storing and playing the noise.
3. A method for evaluating the quality of sound in a vehicle based on physiological signals is characterized by comprising the following steps:
step one, collecting heart rate signals, pulse signals and respiratory signals of a testee under a plurality of running working conditions; obtaining a plurality of physiological signal characteristic parameters under different driving conditions according to the heart rate signal, the pulse signal and the respiration signal; determining objective psychological comprehensive evaluation index values under the multiple driving conditions;
step two, respectively calculating the association degree of each physiological signal characteristic parameter and the objective psychological comprehensive evaluation index value, and obtaining the weight of the physiological signal characteristic parameter according to the association degree;
step three, calculating a physiological comprehensive evaluation index I s ：
Wherein m represents the number of physiological characteristic parameters, W i Weight, X, representing characteristic parameter of i-th physiological signal i "denotes the normalized value of the characteristic parameter of the i-th physiological signal.
4. The physiological signal-based in-vehicle sound quality evaluation method according to claim 3, wherein the driving condition includes: the vehicle is under the conditions of idle speed, vehicle speed of 10km/h, vehicle speed of 20km/h, vehicle speed of 30km/h, vehicle speed of 40km/h, vehicle speed of 50km/h, vehicle speed of 60km/h and vehicle speed of 70 km/h.
5. The method according to claim 4, wherein the physiological signal characteristic parameters comprise: average RR interval, RR interval standard deviation, heart rate low-frequency power, heart rate high-frequency power, heart rate low-frequency power and high-frequency power ratio, pulse adjacent main wave interval mean value, pulse adjacent main wave interval standard deviation, pulse main wave peak value mean value, pulse main wave peak value standard deviation, the percentage of pulse main wave interval more than 50ms, respiration adjacent wave interval mean value, respiration adjacent wave interval standard deviation, respiration adjacent wave interval first-order difference mean value, respiration adjacent wave interval first-order difference standard deviation and respiration adjacent wave interval second-order difference mean value.
6. The method according to claim 5, wherein the objective psychometric synthesis evaluation index value is calculated by:
I p ＝-0.2L′+0.2F′+0.2R′+0.2S′+0.2AI′；
wherein, I p For objective psychology comprehensive evaluation index value, L ' represents normalized value of loudness, F ' represents normalized value of jitter, R ' represents normalized value of roughness, S ' represents normalized value of sharpness, and AI ' represents normalized value of language definition.
7. The method according to claim 5 or 6, wherein the step two of calculating the correlation between each physiological signal characteristic parameter and the objective psychometric synthesis evaluation index value comprises the steps of:
step 1, processing objective psychological comprehensive evaluation index values and physiological signal characteristic parameters to respectively obtain initial value images of the objective psychological comprehensive evaluation index values and the physiological signal characteristic parameters;
step 2, calculating a correlation coefficient between the objective psychological comprehensive evaluation index value and the physiological signal characteristic parameter:
in the formula, gamma 0i (k) The correlation coefficient represents the i-th physiological signal characteristic parameter and the objective psychological comprehensive evaluation index value under the k-th driving condition; i is p ' (k) an initial value image showing objective psychological comprehensive evaluation index values under the kth driving condition; x is the number of i ' (k) represents an initial value image of the characteristic parameter of the ith physiological signal under the kth driving condition, xi is a resolution coefficient, and xi belongs to (0, 1);
step 3, calculating the correlation degree of the physiological signal characteristic parameters and the objective psychological comprehensive evaluation index values:
in the formula, n is the number of running conditions; r is 0i And representing the degree of association between the ith physiological signal characteristic parameter and the objective psychological comprehensive evaluation index value.
8. The method according to claim 7, wherein the step two of obtaining the weight of the physiological signal characteristic parameter comprises:
step a, calculating a weight absolute value of the physiological signal characteristic parameter:
in the formula, W i Represents the weighted absolute value of the ith physiological signal characteristic parameter,
b, determining the sign of the physiological signal characteristic parameter by adopting the following method:
and calculating the covariance of the objective psychological comprehensive evaluation index value and each physiological signal characteristic parameter, wherein if the covariance is a positive value, the weight sign of the physiological signal characteristic parameter is positive, otherwise, the weight sign of the physiological signal characteristic parameter is negative.
9. The physiological signal-based in-vehicle sound quality evaluation method according to claim 8, wherein the resolution coefficient ξ =0.5.
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Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20040260188A1 (en) * | 2003-06-17 | 2004-12-23 | The General Hospital Corporation | Automated auscultation system |
| KR20130127283A (en) * | 2012-05-14 | 2013-11-22 | 인하대학교 산학협력단 | Method for sound quality analysis of vehicle based on brain signal and apparatus for thereof |
| CN103471709A (en) * | 2013-09-17 | 2013-12-25 | 吉林大学 | Method for predicting noise quality of noise inside passenger vehicle |
| CN103687540A (en) * | 2011-05-17 | 2014-03-26 | 大学健康网络 | Osa/csa diagnosis using recorded breath sound amplitude profile and pitch contour |
| CN105021275A (en) * | 2015-07-15 | 2015-11-04 | 吉林大学 | Time domain dynamic characteristic analysis-based intra-vehicle sound quality prediction method |
| CN110068466A (en) * | 2019-04-30 | 2019-07-30 | 山东理工大学 | Vehicle sound quality evaluation method based on brain wave |
| CN110196098A (en) * | 2019-05-23 | 2019-09-03 | 山东理工大学 | Vehicle sound quality evaluation method based on changes in heart rate |
| CN112353391A (en) * | 2020-10-22 | 2021-02-12 | 武汉理工大学 | Electroencephalogram signal-based method and device for recognizing sound quality in automobile |
| US20210052198A1 (en) * | 2019-08-20 | 2021-02-25 | Koninklijke Philips N.V. | System and method of detecting falls of a subject using a wearable sensor |
| CN113727638A (en) * | 2018-12-26 | 2021-11-30 | 生命解析公司 | Method and system for automatically quantizing signal quality |
| CN113884178A (en) * | 2021-09-30 | 2022-01-04 | 江南造船(集团)有限责任公司 | Modeling device and method of noise sound quality evaluation model |
| CN115017974A (en) * | 2022-05-10 | 2022-09-06 | 武汉理工大学 | Sound quality self-shaping design method and system integrating sound quality evaluation |
-
2022
- 2022-09-19 CN CN202211134757.0A patent/CN115444381A/en active Pending
Patent Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20040260188A1 (en) * | 2003-06-17 | 2004-12-23 | The General Hospital Corporation | Automated auscultation system |
| CN103687540A (en) * | 2011-05-17 | 2014-03-26 | 大学健康网络 | Osa/csa diagnosis using recorded breath sound amplitude profile and pitch contour |
| KR20130127283A (en) * | 2012-05-14 | 2013-11-22 | 인하대학교 산학협력단 | Method for sound quality analysis of vehicle based on brain signal and apparatus for thereof |
| CN103471709A (en) * | 2013-09-17 | 2013-12-25 | 吉林大学 | Method for predicting noise quality of noise inside passenger vehicle |
| CN105021275A (en) * | 2015-07-15 | 2015-11-04 | 吉林大学 | Time domain dynamic characteristic analysis-based intra-vehicle sound quality prediction method |
| CN113727638A (en) * | 2018-12-26 | 2021-11-30 | 生命解析公司 | Method and system for automatically quantizing signal quality |
| CN110068466A (en) * | 2019-04-30 | 2019-07-30 | 山东理工大学 | Vehicle sound quality evaluation method based on brain wave |
| CN110196098A (en) * | 2019-05-23 | 2019-09-03 | 山东理工大学 | Vehicle sound quality evaluation method based on changes in heart rate |
| US20210052198A1 (en) * | 2019-08-20 | 2021-02-25 | Koninklijke Philips N.V. | System and method of detecting falls of a subject using a wearable sensor |
| CN112353391A (en) * | 2020-10-22 | 2021-02-12 | 武汉理工大学 | Electroencephalogram signal-based method and device for recognizing sound quality in automobile |
| CN113884178A (en) * | 2021-09-30 | 2022-01-04 | 江南造船(集团)有限责任公司 | Modeling device and method of noise sound quality evaluation model |
| CN115017974A (en) * | 2022-05-10 | 2022-09-06 | 武汉理工大学 | Sound quality self-shaping design method and system integrating sound quality evaluation |
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