A method of screening for pulse sound characteristics that induce emotion

Abstract

The application discloses a kind of induced mood pulse sound feature screening method, belong to cognitive neuroscientific technology field, solve the problem of lack of effective screening in prior art mood pulse sound feature.A kind of induced mood pulse sound feature screening method, the method comprises: under the condition of subject within experiment, according to the scene of inducing mood, set multiple pulse sounds;The independent variable of the pulse sound includes sound frequency, repetition frequency and relative loudness;Different sound frequency, repetition frequency and relative loudness are cross designed, form multiple sound stimuli;Using multiple sound stimuli, a plurality of subjects are experimented respectively;And the physiological signal of each subject under each sound stimulus, emotional valence score and arousal degree score are acquired;Based on the physiological signal of all subjects under all sound stimuli, emotional valence score and arousal degree score, the feature screening result of corresponding induced mood pulse sound is obtained.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of cognitive neuroscience, and particularly relates to a method for screening pulse sound features for inducing emotions. BACKGROUND

[0002] Noise or unwanted sound is an important source of annoyance and distress. With the continuous advancement of urbanization and industrialization, noise is becoming more and more common in living and working environments, such as factory, traffic, car, air conditioner noise, etc. In particular, many machines commonly used in working environments, such as fans, blowers, compressors, etc., will generate low-frequency noise with a frequency below 100 Hz. Notably, many noises exist in the form of pulse sound. Previous studies have shown that emotions are susceptible to noise, and the higher the noise annoyance, the worse the emotional state. A bad emotional state will negatively affect problem solving, and work performance will also be poorer. If the emotional state is good, the way of looking at problems will be more optimistic, and people in this state are more likely to accept the surrounding environment, and their work performance will also be higher.

[0003] Studies have shown that the two dimensions of valence and arousal are suitable for subjective description of emotional response to sound. In addition to subjective evaluation, emotions can be expressed through a variety of physiological information, and various information features can be analyzed to assess the emotional state of participants. Most studies focus on the analysis of facial expressions or speech, but these types of signals (more or less) are easy to fake. In addition, emotions caused by sound can also be evaluated through physiological signals of the peripheral nervous system (such as heart rate, electromyography, skin resistance) and the central nervous system (such as electroencephalogram). Although subjective indicators for evaluating emotions can distinguish different emotional types, they have the disadvantage that emotions tend to dissipate when introspected, and the intensity of emotions decreases when recalled. Therefore, the accuracy of emotion evaluation based solely on subjective experience is not high. Emotion evaluation based on physiological indicators is not affected by the subjective of the measured person, and does not have the problem of intentional or unintentional non-reporting of emotions by the subject, but it is not easy to distinguish various emotions. Therefore, it is more reliable to evaluate emotions by combining subjective evaluation with physiological indicators.

[0004] Current research on the influence of sound on emotions is more common, but research on the influence of pulse sound on emotions is less common, and research on what features of pulse sound have a decisive influence on emotions is even more lacking. Therefore, how to effectively screen pulse sound features that affect emotions, so as to better use pulse sound to regulate emotions, is a technical problem that needs to be solved at present. SUMMARY

[0005] In view of the above analysis, the embodiments of the present application aim to provide a method for screening pulse sound features for inducing emotions, so as to solve the problem of lack of effective screening of pulse sound features that affect emotions in the prior art.

[0006] The application provides a method for screening impulse sound features for inducing emotions, and the method comprises the following steps:

[0007] In an intrasubject experiment condition, a plurality of impulse sounds are set according to scenes for inducing emotions; the independent variables of the impulse sounds include sound frequency, repetition frequency and relative loudness; different sound frequencies, repetition frequencies and relative loudnesses are cross-designed to form a plurality of sound stimuli;

[0008] A plurality of subjects are experimented on by using the plurality of sound stimuli; and physiological signals, emotion valence scores and arousal scores of each subject under each sound stimulus are obtained;

[0009] Based on the physiological signals, emotion valence scores and arousal scores of all subjects under all sound stimuli, feature screening results of impulse sounds for inducing corresponding emotions are obtained.

[0010] Based on the above scheme, the application further makes the following improvements:

[0011] Further, the feature screening results of impulse sounds for inducing corresponding emotions are executed as follows:

[0012] The physiological signals of each subject under each sound stimulus are sequentially preprocessed and feature-extracted to obtain physiological statistical features of the corresponding subject under the corresponding sound stimulus;

[0013] Based on the physiological statistical features of all subjects under each sound stimulus, average physiological statistical features under the corresponding sound stimulus are obtained;

[0014] The emotion valence scores and arousal scores of all subjects under each sound stimulus are averaged to obtain an average emotion valence score and an average arousal score under the corresponding sound stimulus;

[0015] Based on the average physiological statistical features, the average emotion valence score and the average arousal score under all sound stimuli, the feature screening results of impulse sounds for inducing corresponding emotions are obtained.

[0016] Further, in the process of experimenting on the plurality of subjects, N test times are set for each sound stimulus, each test time is regarded as a condition, N is a positive integer; all conditions are randomly presented;

[0017] In the process of playing the sound stimulus in each test time, the physiological signals of the subject in the corresponding test time are collected;

[0018] After the playing of the sound stimulus in each test time is completed, the emotion valence score and the arousal score of the corresponding test time are collected.

[0019] Further, the physiological signals include electrocardiogram signals and corrugator electromyogram signals; and the physiological statistical features include heart rate features, heart rate variability features and corrugator electromyogram features.

[0020] Further, the preprocessing of the electrocardiogram signals comprises, in sequence, removing baseline drift, removing power frequency noise and removing irrelevant electromyogram noise.

[0021] The preprocessing of the corrugator electromyogram signals comprises, in sequence, filtering, removing power frequency noise, rectifying, smoothing and normalizing.

[0022] The rectifying is configured to change all negative half waves of the corrugator electromyogram signal after removing power frequency noise into positive half waves with unchanged amplitudes.

[0023] Further, the heart rate features and the heart rate variability features of the corresponding subject under the corresponding sound stimulation are obtained by performing the following operations.

[0024] The electrocardiogram signals of each subject under each sound stimulation in each trial are respectively preprocessed to obtain preprocessed electrocardiogram signals of the corresponding subject under the corresponding sound stimulation in the corresponding trial, and the heart rate and the heart rate variability are extracted from the preprocessed electrocardiogram signals.

[0025] The heart rate and the heart rate variability of the corresponding subject under the corresponding sound stimulation in all trials are respectively averaged to obtain the heart rate features and the heart rate variability features of the corresponding subject under the corresponding sound stimulation.

[0026] Further, the corrugator electromyogram features of the corresponding subject under the corresponding sound stimulation are obtained by performing the following operations.

[0027] The corrugator electromyogram signals of each subject under each sound stimulation in each trial are respectively preprocessed to obtain preprocessed corrugator electromyogram signals of the corresponding subject under the corresponding sound stimulation in the corresponding trial, and the average amplitude of the corrugator electromyogram is extracted from the preprocessed corrugator electromyogram signals.

[0028] The average amplitudes of the corrugator electromyogram of the corresponding subject under the corresponding sound stimulation in all trials are averaged to obtain the corrugator electromyogram features of the corresponding subject under the corresponding sound stimulation.

[0029] Further, the average physiological statistical features include average heart rate features, average heart rate variability features and average corrugator electromyogram features; and the average physiological statistical features under the corresponding sound stimulation are obtained by performing the following operations.

[0030] The heart rate features, the heart rate variability features and the corrugator electromyogram features of all subjects under each sound stimulation are respectively averaged to obtain the average heart rate features, the average heart rate variability features and the average corrugator electromyogram features under the corresponding sound stimulation.

[0031] Further, the method further comprises:

[0032] performing repeated measurement analysis of variance between the independent variables and dependent variables of the impulse sound under various sound stimuli, wherein the dependent variables of the impulse sound include average physiological statistical characteristics, mean of emotional valence score and mean of arousal score;

[0033] checking whether the repeated measurement analysis of variance passes by using spherical test, if the spherical test does not pass, correcting the degrees of freedom and statistical values in the result of the repeated measurement analysis of variance to update the result of the repeated measurement analysis of variance;

[0034] according to the result of the repeated measurement analysis of variance, evaluating the correlation between the independent variables and dependent variables of the impulse sound, and screening the independent variable with the greatest impact on emotion in the impulse signal as the characteristic screening result of the impulse sound inducing emotion.

[0035] Further, the method further comprises:

[0036] Before the experiments on the multiple subjects are performed respectively, the method further comprises the steps of presenting the experimental instructions to each subject respectively, and requiring each subject to perform a practice experiment

[0037] Compared with the prior art, the present application can achieve at least one of the following beneficial effects:

[0038] The method for screening the characteristics of the impulse sound inducing emotion provided by the present application can effectively screen the main impulse sound characteristics affecting emotion, so that the impulse sound can be better used to regulate emotion, and the problem of lacking effective screening of the impulse sound characteristics affecting emotion in the prior art is well solved.

[0039] The above technical solutions can be combined with each other in the present application to achieve more preferred combination solutions. Other features and advantages of the present application will be described in the subsequent specification, and some advantages will become apparent from the specification, or will be understood by implementing the present application. The purposes and other advantages of the present application can be achieved and obtained from the contents specifically pointed out in the specification and the drawings. BRIEF DESCRIPTION OF DRAWINGS

[0040] The accompanying drawings are included to provide a further understanding of the present application, and are incorporated herein and constitute a part of the detailed description. The drawings illustrate embodiments of the present application and, together with the description, serve to explain the principles of the present application. In the drawings:

[0041] Figure 1 a flowchart of the method for screening the characteristics of the impulse sound inducing emotion provided by the embodiment of the present application;

[0042] Figure 2Another flowchart of the method for screening the pulse sound features for inducing emotions provided by the embodiment of the present application is shown in FIG. 2.

[0043] Figure 3 A flowchart of the experimental trial provided by the embodiment of the present application is shown in FIG. 3.

[0044] Figure 4 A comparison diagram of the valence of the subjects under different sound stimuli provided by the embodiment of the present application is shown in FIG. 4.

[0045] Figure 5 A comparison diagram of the arousal of the subjects under different sound stimuli provided by the embodiment of the present application is shown in FIG. 5.

[0046] Figure 6 A comparison diagram of the corrugator electromyography amplitude (RMS) of the subjects under different sound stimuli provided by the embodiment of the present application is shown in FIG. 6.

[0047] Figure 7 A comparison diagram of the heart rate (BMP) of the subjects under different sound stimuli provided by the embodiment of the present application is shown in FIG. 7. DETAILED DESCRIPTION

[0048] The preferred embodiments of the present application will be described in detail below with reference to the drawings, which form a part of this application. The drawings and the associated descriptions are provided to illustrate the preferred embodiments of the present application and to explain the principles of the present application, but are not intended to limit the scope of the present application.

[0049] One specific embodiment of the present application discloses a method for screening the pulse sound features for inducing emotions, and a flowchart is shown in FIG. 1. Figure 1 The specific implementation steps of the method are described as follows.

[0050] Step S1: Under the experimental conditions of the subjects, multiple pulse sounds are set according to the scenes for inducing emotions; the independent variables of the pulse sounds include sound frequency, repetition frequency and relative loudness; different sound frequencies, repetition frequencies and relative loudnesses are cross-designed to form multiple sound stimuli.

[0051] Step S2: Multiple subjects are experimented by using the multiple sound stimuli; and the physiological signals, emotional valence scores and arousal scores of each subject under each sound stimulus are obtained.

[0052] Preferably, in the process of experimenting on the multiple subjects, N trial times are set for each sound stimulus, each trial time is regarded as a condition, N is a positive integer; all conditions are randomly presented. In the process of playing the sound stimulus of each trial time, the physiological signals of the subject of the corresponding trial time are collected; after the playing of the sound stimulus of each trial time is ended, the emotional valence scores and arousal scores of the corresponding trial time are collected.

[0053] In the embodiment, the physiological signals affected by the sound stimulation of the impulse sound mainly include electrocardiogram signals and corrugator electromyogram signals.

[0054] Step S3: Based on the physiological signals of all subjects under all sound stimulations, the emotional valence scores and the arousal scores, the characteristic screening result of the impulse sound corresponding to the induced emotion is obtained.

[0055] In step S3, the following is specifically performed:

[0056] Step S31: The physiological signals of each subject under each sound stimulation are sequentially preprocessed and feature-extracted respectively to obtain the physiological statistical features of the corresponding subject under the corresponding sound stimulation.

[0057] Specifically, in the embodiment, the preprocessing of the electrocardiogram signals sequentially includes removing baseline drift, removing power frequency noise and removing irrelevant electromyogram noise. The preprocessing of the corrugator electromyogram signals sequentially includes filtering, removing power frequency noise, rectification, smoothing, and normalization. The rectification is used to change all negative half waves of the corrugator electromyogram signal after removing power frequency noise into positive half waves with unchanged amplitudes. In the embodiment, the physiological statistical features include heart rate features, heart rate variability features and corrugator electromyogram features.

[0058] In step S31, the heart rate features and the heart rate variability features of the corresponding subject under the corresponding sound stimulation are obtained by performing the following operations:

[0059] Step S311: The electrocardiogram signals of each subject under each sound stimulation in each trial are preprocessed respectively to obtain the preprocessed electrocardiogram signals of the corresponding subject under the corresponding sound stimulation in the corresponding trial, and the heart rate and the heart rate variability are extracted from the preprocessed electrocardiogram signals.

[0060] Step S312: The heart rate and the heart rate variability of the corresponding subject under the corresponding sound stimulation in all trials are averaged respectively to obtain the heart rate features and the heart rate variability features of the corresponding subject under the corresponding sound stimulation.

[0061] In addition, in step S31, the corrugator electromyogram features of the corresponding subject under the corresponding sound stimulation are also obtained by performing the following operations:

[0062] Step S313: The corrugator electromyogram signals of each subject under each sound stimulation in each trial are preprocessed to obtain the preprocessed corrugator electromyogram signals of the corresponding subject under the corresponding sound stimulation in the corresponding trial, and the mean value of the corrugator electromyogram amplitude is extracted from the preprocessed corrugator electromyogram signals.

[0063] Step S314: The mean value of the corrugator electromyogram amplitude of the corresponding subject under the corresponding sound stimulation in all trials is averaged to obtain the corrugator electromyogram features of the corresponding subject under the corresponding sound stimulation.

[0064] Step S32: Obtain the average physiological statistical features under the corresponding sound stimulus according to the physiological statistical features of all subjects under each sound stimulus.

[0065] In this embodiment, the average physiological statistical features include average heart rate features, average heart rate variability features and average corrugator electromyography features.

[0066] At this time, in step S32, the average physiological statistical features under the corresponding sound stimulus are obtained, and the following is performed:

[0067] The heart rate features, heart rate variability features and corrugator electromyography features of all subjects under each sound stimulus are averaged respectively, and the average heart rate features, average heart rate variability features and average corrugator electromyography features under the corresponding sound stimulus are obtained correspondingly.

[0068] Step S33: Average the emotional valence scores and arousal scores of all subjects under each sound stimulus respectively to obtain the average emotional valence score and the average arousal score under the corresponding sound stimulus.

[0069] Step S34: Obtain the feature screening result of the pulse sound inducing the emotion based on the average physiological statistical features, the average emotional valence score and the average arousal score under all sound stimuli.

[0070] In step S34, the feature screening result of the pulse sound inducing the emotion can be determined by repeated measurement variance analysis. The specific process is explained as follows:

[0071] Step S341: Perform repeated measurement variance analysis on the relationship between the independent variables and dependent variables of the pulse sound under various sound stimuli; wherein the dependent variables of the pulse sound include the average physiological statistical features, the average emotional valence score and the average arousal score;

[0072] Step S342: Check whether the repeated measurement variance analysis passes by using spherical test method, and if the spherical test does not pass, correct the degrees of freedom and statistical values in the result of the repeated measurement variance analysis to update the result of the repeated measurement variance analysis;

[0073] Step S343: According to the result of the repeated measurement variance analysis, assess the correlation between the independent variables and dependent variables of the pulse sound, and screen out the independent variable with the greatest impact on the emotion in the pulse signal as the feature screening result of the pulse sound inducing the emotion.

[0074] In step S34, the feature screening result of the pulse sound inducing the emotion can also be determined by multivariate linear regression analysis method and Durbin-Watson test method. The specific process is explained as follows:

[0075] A hierarchical regression model is established by using a multiple linear regression analysis method, and independent variables of the pulse sound are added one by one, so as to investigate the relationship between the independent variables and dependent variables of the pulse sound under various sound stimuli;

[0076] R 2 As a reference, the explanatory rate of the independent variables of the pulse sound under different sound stimuli on the corresponding dependent variables of the pulse sound is investigated (the higher the explanatory rate, the more important the independent variable); in combination with the Debin-Watson test method, the independent variable with the greatest influence on the emotion in the pulse signal is screened as the characteristic screening result of the pulse sound for inducing emotion.

[0077] In addition, in order to improve the execution efficiency and reliability of the method in the embodiment, the method further comprises:

[0078] Before the experiments on the plurality of subjects are respectively performed, the method further comprises the steps of presenting an experimental instruction to each subject respectively, and requiring each subject to perform a practice experiment respectively.

[0079] Another specific embodiment of the present application provides a specific implementation process of the characteristic screening method of the pulse sound for inducing emotion, and a flowchart is shown as Figure 2 The following is explained.

[0080] (1) Experimental task design

[0081] The present experiment adopts an intra-subject experimental design, which is a true experimental design in which each or each group of subjects receives all experimental treatments of the independent variables, also known as repeated measurement design or within-group design. In the embodiment, the following settings are made according to the scene for inducing emotion: the sound frequency is set to 5000 Hz, 10000 Hz and 15000 Hz, the repetition frequency is set to 1 Hz, 100 Hz and 400 Hz, and the relative loudness is set to -12 db and -24 db. The dependent variables of the pulse noise are the physiological signals of the subjects hearing the sound stimuli, as well as the emotion valence score and arousal score (i.e., the scores of the subjects rating the emotion valence and arousal). In the specific implementation process, the emotion valence score and arousal score can be quantified by using a 9-point rating scale, for example: the valence is a 9-point rating scale, from -4 to 4, -4 represents very unpleasant, and 4 represents very pleasant; the arousal is a 9-point rating scale, from -4 to 4, -4 represents very calm, and 4 represents very excited.

[0082] (2) Experimental procedure

[0083] ① First, the experimental instruction is presented to the subjects, and the experimental content and experimental response method are explained.

[0084] Secondly, the subjects were asked to do the practice experiment, a total of 10 trials. The process of each trial of the practice experiment was as follows: a fixation point was presented in the center of the screen for 1000 ms, and then an empty screen was presented for 1000 ms. This form is commonly used in psychology and cognitive science research. The fixation point presented in the screen is to make the subjects concentrate attention, and the empty screen is presented to reduce the interference of other factors on attention, so as to better listen to the following sound stimulus. Then the sound stimulus was played for 5s. After the sound stimulus was played, the subjects were asked to move the mouse to score the valence and arousal of the emotions displayed on the screen according to the emotions they felt when they heard the sound stimulus, and then enter the next trial.

[0085] Thirdly, after each subject was familiar with the requirements of the practice experiment, the formal experiment was entered. The process was the same as the practice trial, and there were 20 trials for each condition. The experiment had a total of 360 trials. Each condition was randomly presented. There was a break in the middle of the formal experiment, about 3-5 minutes.

[0086] The flow chart of the experimental trial is shown in Figure 3 .

[0087] (3) Data collection

[0088] The physiological data of the subjects were collected by the MP150 multi-channel physiological recorder of BIOPAC, with a sampling rate of 1000 Hz, using a high-pass filter of 0.5 Hz and a low-pass filter of 35 Hz. The physiological signals of the subjects were recorded during the playing of the sound stimulus, including the corrugator electromyogram, electrocardiogram signal (heart rate and heart rate variability were obtained after processing), etc. The data collected by the multi-channel physiological recorder did not include the data of the evaluation stage (i.e. when the subjects evaluated their emotional feelings when they heard the sound), so as not to cause confusion.

[0089] (4) Data processing

[0090] 1) Preprocessing of electrocardiogram signal:

[0091] ① Remove baseline drift: First, design two median filters, i.e. pass the electrocardiogram signal through a 200ms median filter to remove QRS complex and P wave; then pass it through a 600ms median filter to remove T wave, and get the drifting baseline; finally, subtract the drifting baseline from the electrocardiogram signal to get the signal after filtering the baseline drift.

[0092] ② Remove power frequency noise: Power frequency interference has a fixed frequency, and a 50Hz notch filter is used to notch process the signal after filtering the baseline drift to filter out power frequency interference.

[0093] ③Remove irrelevant myoelectric noise: Irrelevant myoelectric noise (such as some myoelectric noise caused by action, blinking or sweating) interferes with high-frequency interference, while the frequency of the electrocardiogram signal is mainly concentrated in 5-20 Hz, so the electrocardiogram signal filtered by the power frequency interference is filtered by a 5-20 Hz band-pass filter to reduce the influence of irrelevant myoelectric noise.

[0094] 2) Frown myoelectric signal preprocessing:

[0095] ①Filtering: Since the frequency spectrum of the frown myoelectric signal is mainly distributed between 28-500 Hz, a 28-500 Hz band-pass filter is used to filter the frown myoelectric signal;

[0096] ②Remove power frequency noise: A 50 Hz notch filter is used to notch process the filtered frown myoelectric signal to filter out power frequency interference;

[0097] ③Rectification: All negative half waves in the signal are changed to positive half waves, and the amplitude remains unchanged (i.e. the amplitude is retained).

[0098] ④Smoothing: The rectified signal is smoothed using a moving average method to remove high-frequency noise.

[0099] ⑤Normalization: The smoothed signal is normalized to have an amplitude range between 0 and 1.

[0100] Subsequently, the heart rate and heart rate variability can be extracted from the preprocessed electrocardiogram signal by a peak detection algorithm (see the existing method, which will not be described here), and the frown myoelectric amplitude mean value can be extracted from the preprocessed frown myoelectric signal (i.e. all frown myoelectric amplitudes in the preprocessed frown myoelectric signal are averaged to obtain the frown myoelectric amplitude mean value). The heart rate and heart rate variability of each subject under each sound stimulus in all trials are averaged respectively to obtain the heart rate characteristics and heart rate variability characteristics of the corresponding subject under the corresponding sound stimulus. The frown myoelectric amplitude mean value of each subject under each sound stimulus in all trials is averaged to obtain the frown myoelectric characteristics of the corresponding subject under the corresponding sound stimulus. Then, the heart rate characteristics, heart rate variability characteristics and frown myoelectric characteristics of all subjects under each sound stimulus are averaged respectively to obtain the average heart rate characteristics, average heart rate variability characteristics and average frown myoelectric characteristics under the corresponding sound stimulus. In addition, the emotional valence score and arousal score of all subjects under each sound stimulus are directly averaged respectively to obtain the emotional valence score mean value and arousal score mean value under the corresponding sound stimulus.

[0101] (5) Data statistical analysis

[0102] The changes of emotional scores (average emotional valence score, average arousal score) and physiological indicators (average heart rate characteristics, average heart rate variability characteristics, average corrugator muscle electrical characteristics) induced by different sound stimuli were analyzed by repeated measurement ANOVA. When the spherical test failed, the degrees of freedom F and the statistical value p were corrected by the greenhouse-geisser method. The spherical degree test was used to test whether the variances of the differences between different measurements were equal. If the spherical degree test p value was less than 0.05, it failed, and the p value needed to be corrected when the spherical degree test failed.

[0103] Secondly, a hierarchical regression model can also be established using multiple linear regression analysis algorithm, gradually including the independent variables of pulsed sound, respectively, to investigate the relationship between different sound stimuli and physiological indicators and emotional scores (i.e., the relationship between the independent variables and dependent variables of pulsed sound under various sound stimuli). At the same time, R 2 Indicators were used as a reference to investigate the explanatory rate of each sound stimulus on physiological indicators and emotional scores. The Durbin-Watson test was used to test whether there was autocorrelation in the residual term, and the collinearity diagnosis was used to determine whether the multiple variables were related to each other and the degree of association. After testing, the data of this study met all the assumptions of linear regression.

[0104] (6) Research results

[0105] ① Analysis results of emotional valence

[0106] Firstly, the emotional valence data of all subjects were analyzed by 3(frequency: 5000hz, 10000hz, 15000hz) x 3(repetition frequency: 1hz, 100hz, 400hz) x 2(loudness: -12db, -24db) repeated measurement ANOVA.

[0107] The results showed that the main effect of frequency (i.e., sound frequency) was significant, F(2, 38) = 30.16, p < 0.001, and the effect size η 2 p = 0.61. After comparing the emotional valence of subjects under three frequency conditions, it was found that the emotional valence level of subjects was the lowest when presenting 5000hz sound, and the emotional valence level of subjects was the highest when presenting 15000hz sound, i.e., the lower the frequency of the presented sound, the more negative emotions the subjects showed. The main effect of repetition frequency (i.e., repetition frequency) was significant, F(2, 38) = 53.70, p < 0.001, η 2 p= 0.74. After pairwise comparison of the three frequency conditions, it was found that the subjects would produce positive emotions when the pulse sound frequency was 1 Hz, but would produce negative emotions when the pulse sound frequency was 100 Hz and 400 Hz, and the emotional valence level was the lowest when the frequency was 100 Hz. The main effect of loudness (i.e., relative loudness) was significant, F(1, 39) = 94.29, p < 0.001, η 2 p = 0.71. After comparing the emotional valence of the subjects under the two relative loudness conditions, it was found that the subjects would produce more negative emotions when the-12db loudness sound was presented. The results of the main effect of each variable are shown in Figure 4

[0108] The results of the multiple regression analysis showed that the degree of explaining the variation of emotional valence was 6% when modeling with the loudness index, 6% when modeling with the frequency variable, and 44% when modeling with the repeated frequency sound variable, which increased the prediction of emotional valence by 38%. Therefore, the repeated frequency had a more significant impact on emotional valence.

[0109] ② Analysis results of emotional arousal

[0110] The results of repeated measures ANOVA showed that the main effect of frequency was significant, F(2, 38) = 18.32, p < 0.001, η 2 p = 0.49. After further pairwise comparison of the three conditions, it was found that the arousal level induced by 5000 Hz sound was greater than that induced by 10000 Hz, and the arousal level induced by 10000 Hz was greater than that induced by 5000 Hz. The main effect of repeated frequency was significant, F(2, 38) = 35.82, p < 0.001, η 2 p = 0.65. The significant difference of repeated frequency showed that the arousal level of the subjects was the lowest when the repeated frequency was 1 Hz, and the arousal level of the subjects was the highest when the repeated frequency was 100 Hz. The main effect of loudness was significant, F(1, 39) = 63.37, p < 0.001, η 2 p = 0.62. The specific performance of the subjects under different loudness conditions was that the arousal level of the subjects was greater when the-12db loudness sound was presented than when the-24db loudness sound was presented. The results of the main effect of each variable are shown in Figure 5 .

[0111] ​The multiple regression analysis results show that the loudness index can explain 4% of the variation in emotional arousal, and the frequency variable can also explain 4% of the variation in emotional arousal. When the repetition frequency variable is added to the model, the prediction of emotional arousal increases by 25%, and the repetition frequency variable has the greatest impact on emotional arousal.

[0112] ③ Analysis results of corrugator muscle EMG amplitude

[0113] The repeated measures ANOVA results show that the main effect of repetition frequency is significant, F(2, 34) = 4.41, p < 0.05, η 2 p = 0.21. After pairwise comparison, it is found that the corrugator muscle EMG amplitude of the subjects under the condition of 400 Hz repetition frequency sound is greater than that under the condition of 1 Hz repetition frequency sound. The main effect of loudness is also significant, F(1, 35) = 7.06, p < 0.05, η 2 p = 0.17. After analyzing the muscle EMG data under different loudness conditions, it is found that the corrugator muscle EMG amplitude of the subjects under the condition of -24 db loudness is greater than that under the condition of -12 db loudness. The main effect of frequency is not significant, F(2, 34) = 1.43, p = 0.25, indicating that the difference in corrugator muscle changes of the subjects under different frequency sounds is not significant. This indicates that the repetition frequency and loudness of sound can independently affect the changes in corrugator muscle, but the effect of frequency is not obvious. The results of the main effects of each variable are shown in Figure 6 .

[0114] The multiple regression analysis results show that loudness can explain 1% of the changes in corrugator muscle, and the frequency variable can also explain 1% of the changes in corrugator muscle. After adding the repetition frequency variable, it is found that it can predict 2% of the changes in corrugator muscle, increasing by 1%. Therefore, loudness and repetition frequency can be used as influencing factors to predict the changes in corrugator muscle.

[0115] ④ Analysis results of heart rate (BPM)

[0116] The repeated measures ANOVA results show that the main effect of repetition frequency is significant, F(2, 31) = 16.26, p < 0.001, η 2 p = 0.51. Further analysis shows that under the condition of 100 Hz, the heart rate of the subjects is the fastest, and under the condition of 1 Hz, the heart rate of the subjects is the slowest. The main effect of frequency is marginally significant, F(2, 31) = 16.26, p = 0.09, η 2 p= 0.15, the subjects hear 15000hz sound faster than 5000hz, 5000hz sound faster than 10000hz. But the main effect of loudness is not significant, F(1, 32) = 2.13, p = 0.16, which shows that there is no significant difference in the heart rate of subjects when presenting different loudness of sound. The results of BPM under different experimental conditions are shown in Table 1. Figure 7 .

[0117] The model established by multiple regression analysis shows that the independent explanation of the effects of loudness, frequency and repetition frequency on heart rate change is not obvious.

[0118] According to the results of the subjective evaluation scale and the objective physiological indicators, although multiple characteristics of the pulse sound can affect the emotion, the effect of the repetition frequency on the emotion is the most obvious, so the repetition frequency can be mainly selected as the main research factor to adjust the emotion in the follow-up.

[0119] In summary, the pulse sound feature screening method for inducing emotion proposed in the embodiment can effectively screen the pulse sound features that affect the emotion, so that the pulse sound can be better used to adjust the emotion.

[0120] Those skilled in the art can understand that all or part of the processes of the above-mentioned embodiment methods can be completed by a computer program instructing related hardware, and the program can be stored in a computer readable storage medium. The computer readable storage medium includes a magnetic disk, an optical disk, a read-only memory, a random access memory, etc.

[0121] The above is only a preferred specific embodiment of the present application, but the protection scope of the present application is not limited to this. Any changes or replacements within the technical scope disclosed by the present application can be easily thought by those skilled in the art, and should be covered within the protection scope of the present application.

Claims

1. A method for screening impulse sound features that induce emotions, characterized in that, The method includes: Under in-subject experimental conditions, various impulse sounds were set according to the scenarios that evoked emotions; the independent variables of the impulse sounds included sound frequency, repetition frequency, and relative loudness; different sound frequencies, repetition frequencies, and relative loudnesses were cross-designed to form a variety of sound stimuli; Multiple subjects were tested using various auditory stimuli; and physiological signals, emotional valence scores, and arousal scores were obtained for each subject under each auditory stimulus. Based on the physiological signals, emotional valence scores, and arousal scores of all subjects under all sound stimuli, the feature screening results of the corresponding impulse sounds that induce emotions were obtained. Among them, the correlation between the independent and dependent variables of the impulse sounds was evaluated, and the independent variable with the greatest impact on emotions in the impulse signals was selected as the feature screening results of the impulse sounds that induce emotions. The dependent variables for impulse sounds include mean physiological statistical characteristics, mean emotional valence score, and mean arousal score.

2. The method for screening impulse sound features that induce emotions according to claim 1, characterized in that, The feature filtering results of the corresponding impulse sounds that induce emotions are obtained, and then the following steps are performed: The physiological signals of each subject under each type of sound stimulus were preprocessed and feature extracted sequentially to obtain the physiological statistical characteristics of the corresponding subject under the corresponding sound stimulus. Based on the physiological statistical characteristics of all subjects under each sound stimulus, the average physiological statistical characteristics under the corresponding sound stimulus are obtained; The emotional valence scores and arousal scores of all subjects under each type of sound stimulus were averaged to obtain the mean emotional valence scores and mean arousal scores for the corresponding sound stimulus. Based on the average physiological statistical characteristics, mean emotional valence score, and mean arousal score of all sound stimuli, the feature screening results of the corresponding impulse sounds that induce emotions were obtained.

3. The method for screening impulse sound features that induce emotions according to claim 2, characterized in that, During the experiment with multiple subjects, each sound stimulus was set with N trials, each trial being a condition, where N is a positive integer; all conditions were presented randomly. During each trial of sound stimulus playback, physiological signals of the subjects in that trial were collected. After each trial of sound stimulation, the emotional valence score and arousal score for that trial were collected.

4. The method for screening impulse sound features that induce emotions according to claim 3, characterized in that, The physiological signals include electrocardiogram (ECG) signals and brow brow electromyography (EMG) signals; the physiological statistical features include heart rate features, heart rate variability features, and brow brow EMG features.

5. The method for screening impulse sound features that induce emotions according to claim 4, characterized in that, The preprocessing of the electrocardiogram signal includes, in sequence: removing baseline drift, removing power line noise, and removing irrelevant electromyographic noise; The preprocessing of the frowning electromyography signal includes, in sequence: filtering, removal of power frequency noise, rectification, smoothing, and normalization. The rectification is used to convert all the negative half-waves of the frowning electromyography signal after removing power frequency noise into positive half-waves, while keeping the amplitude unchanged.

6. The method for screening impulse sound features that induce emotions according to claim 5, characterized in that, The heart rate characteristics and heart rate variability characteristics of the corresponding subjects under the corresponding sound stimulation were obtained by performing the following operations: For each subject under each sound stimulus, the electrocardiogram (ECG) signal was preprocessed in each trial to obtain the preprocessed ECG signal of the corresponding subject under the corresponding sound stimulus in the corresponding trial, and the heart rate and heart rate variability were extracted from the preprocessed ECG signal. The heart rate and heart rate variability of the corresponding subjects under the corresponding sound stimulus were averaged over all trials to obtain the heart rate characteristics and heart rate variability characteristics of the corresponding subjects under the corresponding sound stimulus.

7. The method for screening impulse sound features that induce emotions according to claim 6, characterized in that, The following steps were performed to obtain the corresponding electromyographic characteristics of the frowning muscles of the subjects under the corresponding sound stimulation: For each subject under each sound stimulus, the brow brow electromyography (EMG) signal of each subject in each trial was preprocessed to obtain the preprocessed brow brow EMG signal of the corresponding subject under the corresponding sound stimulus in the corresponding trial, and the mean amplitude of the brow brow EMG was extracted from the preprocessed brow brow EMG signal. The average amplitude of the brow brow electromyography (EMG) of the corresponding subject under the corresponding sound stimulus was calculated across all trials to obtain the brow brow EMG characteristics of the corresponding subject under the corresponding sound stimulus.

8. The method for screening impulse sound features that induce emotions according to claim 7, characterized in that, The average physiological statistical characteristics include average heart rate characteristics, average heart rate variability characteristics, and average frowning electromyography characteristics; to obtain the average physiological statistical characteristics under the corresponding sound stimulation, the following steps are performed: The heart rate characteristics, heart rate variability characteristics, and brow furrow electromyography characteristics of all subjects under each sound stimulus were averaged to obtain the average heart rate characteristics, average heart rate variability characteristics, and average brow furrow electromyography characteristics under the corresponding sound stimulus.

9. The method for screening impulse sound features that induce emotions according to claim 8, characterized in that, The feature filtering results of the corresponding impulse sounds that induce emotions are obtained, and then the following steps are performed: Repeated measures ANOVA was performed to investigate the relationship between the independent and dependent variables of impulse sounds under various sound stimuli. The test of sphericity is used to check whether the repeated measures ANOVA passes. If the test of sphericity fails, the degrees of freedom and statistical values ​​in the repeated measures ANOVA results are corrected to update the repeated measures ANOVA results. Based on the results of repeated measures ANOVA, the association between the independent and dependent variables of impulse sounds was evaluated, and the independent variable with the greatest impact on emotions in the impulse signal was selected as the feature screening result of impulse sounds that induce emotions.

10. The method for screening impulse sound features that induce emotions according to any one of claims 1-9, characterized in that, The method further includes: Before conducting experiments on multiple subjects, the experiment also includes steps such as presenting experimental instructions to each subject and requiring each subject to perform practice experiments.

Images