# Optimized Piano Music Education Model Based on Multimodal Information Fusion for Emotion Recognition in Multimedia Video Networks

Authors: Jie Bai
Journal: Mobile Information Systems
 (2022)

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## Abstract

Emotion is the important information that people transmit in the process of communication, and the change of emotional state affects people’s perception and decision-making, which introduces the emotional dimension into human-computer interaction. The modes of emotional expression include facial expressions, speech, posture, physiological signals, text, and so on. Emotion recognition is essentially a multimodal fusion problem. This paper investigates the different teaching modes of the teachers and students of our school, designs the load capacity through the K-means algorithm, builds a multimedia network sharing classroom, and creates a piano music situation to stimulate students’ learning interest, using audiovisual and other tools to mobilize students’ emotions, using multimedia guidance to extend students’ piano music knowledge, and comprehensively improve students’ aesthetic ability and autonomous learning ability. Comparing the changes of students after 3 months of teaching, the results of the study found that multimedia sharing classrooms can be up to 50% ahead of traditional teaching methods in enhancing students’ interest, and teachers’ acceptance of multimedia network sharing classrooms is also high.


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## Body

## 1. Introduction

Emotion recognition introduces the emotional dimension into human–computer interaction. The research of emotion recognition covers many disciplines, and its related research directions include a wide range of applications in the fields of intelligent human–computer interaction, medical care, assistance for the disabled, driving of automobiles and airplanes, safety, and communications. Use of computers for multimedia teaching and multimedia network teaching has increasingly become the main teaching method. The application of this computer-centric modern teaching technology helps in teaching promotion of the development of education. According to the teaching requirements of the gradual development of quality education from elementary education to test-oriented education, it provides the development of the “creative” model. The necessary teaching conditions are established. The further integration of piano music education with multimedia and multimedia network technologies is also a major trend in the development. Piano music evaluation courses can make boring piano music evaluation courses lively and make piano music knowledge simple, clear, and easy to accept. Through the combination of text, images, sound, animation, and so on, the piano music assessment of classroom content is enriched and interesting, and the shortcomings of traditional teaching are effectively improved [1].The modalities of emotional expression include facial expressions, speech, posture, physiological signals, text, and so on [2]. Among them, facial expressions are obtained by collecting facial images, voice emotions are extracted from voice signals with emotions, and body movements more directly express people’s emotions. Using physiological signals such as blood pressure, pulse, skin electricity, and brain electricity, it can be found that people deliberately for hidden emotions, using natural language processing technology can read emotions between lines [3]. Today, most educators use only one method of multimedia technology. They only use slides instead of writing on the blackboard or using videos to replace all the content of the lecture. Although this multimedia teaching method greatly facilitates the teaching of teachers and reduces the time for writing on the blackboard, this method does not improve classroom teaching. In the piano music assessment class, the traditional piano music teaching method makes students not interested in learning piano music, and the single teaching method makes students bored. Let students fall in love with piano music lessons and cultivate their self-confidence, and independence has become the main task for piano music teachers.Chou et al. by sharing the classroom, 16 young people aged 4–6 were observed and interviewed. The results found that through sharing, the reading motivation of children is promoted, including interest, perceptual control, collaboration, participation, and effectiveness [4]. Lee investigated the ways in which teachers promote children’s reading habits by providing literacy opportunities in an unstructured learning environment and explored the background of children’s participation in independent reading activities. Fifteen children were observed in the university preschool classroom for 7 months [5]. Taylor et al. obtained a statewide compulsory reading comprehension score from the Class ORF Gain variable. The results found that the impact of the shared environment is low, so genetic impact is most relevant to explain this variability [6]. HLD aims to investigate whether EFL teachers’ beliefs are related to each other. The results show that teachers have a positive attitude towards the importance, use, professional knowledge, and background of using digital technology in EFL classrooms [7]. Panero and Aldon observed the 9th-grade classroom using tablet computers [8]. Wang et al. developed teaching of martial arts courses. Through the example verification and statistical analysis of martial arts students in a certain university, the preliminary attempt of multimedia teaching in practical application has been realized [9]. Zhou analyzed the application of multimedia network teaching platform [10]. These studies have provided some references for this article, not been widely recognized. And the lack of data justification makes the research nonexplanatory.Extending the real classroom to the Internet through topic sharing, so that more students can participate in the real-time learning and interaction of the online classroom [11]. Through high-quality video and audio interaction, remote students can take classes online in front of the computer. The learning mode is just like in a real classroom, enabling unimpeded communication and discussion with teachers and students. It is proposed to enhance students’ subjectivity and spontaneous interactive behavior by exploring the contextual task-oriented piano music interactive [12]. By helping and guiding students to cooperate with each other, the interaction between students is brought closer. The results of the study found that multimedia-sharing classrooms are comparable in terms of enhancing students’ interest. The traditional teaching method is 50% ahead, and the teachers’ acceptance of the multimedia network sharing classroom is also high.


## 2. Realization Method of Media Video Network Sharing

### 2.1. Multimedia

Social psychology believes that facial expressions are an important mode of interpersonal communication, and they contribute the most to emotional expression. Researchers use cameras to obtain facial images with facial expression information, extract facial expression features from them, and use them to identify human emotional states. Many researchers obtain expression features by transforming the whole or part of the image. Commonly used image transformation methods include discrete cosine transform, Haar transform, and so on. In order to accurately extract facial expression features. The AdaBoost algorithm is a fast algorithm that can be used for face detection. Facial expression image databases commonly used in experiments include JAFFE and so on. JAFFE is the database of Japanese female facial expressions. There are 213 facial expressions in the database, consisting of seven facial expressions of 10 women.Education requires not only a solid cultural literacy but also the mastery of various quality abilities [13, 14]. To vigorously develop quality education is inseparable from the use. In the teaching of piano music appreciation class, it is necessary to learn to rely on modern multimedia technology to guide students to experience, appreciate, and understand piano music in various ways, to stimulate students’ musical imagination and creativity, as well as their love for piano music learning [15].Change people’s living standards has also become a necessary way for people’s spiritual and cultural progress [16]. Piano music forms as art show a distinction between good and bad, and students have poor ability to discriminate piano music due to age, psychological, and cognitive constraints and are easily affected by the environment. Many excellent piano music works are used by students. Think it is not good, but popular songs and pop piano music are more popular with students. Under such circumstances, piano music teachers cultivate students’ love for excellent works. Therefore, improving the requirements for students, strengthening the guidance of students, and cultivating students’ good ability to appreciate piano music have also become important part of piano music lessons. Enthusiasm for learning allows students to understand classic piano music works more vividly, instead of learning with a feeling of rejection [17]. Multimedia technology in piano music appreciation classes has become an irreversible trend of the times.Multimedia uses text, photos, images, audio, video, courses, and other formats to simulate situations [18]. In the simulated situation, students can enter their own environment to complete the evaluation and analysis of musical works [19, 20]. Teachers’ preaching and repeated supervision are not as good as students’ interest, which interested in use multimedia technology to create piano music situations in the teaching process, download images, videos, and other content-related materials to pay attention to create a situation that is conducive to students’ learning can greatly interest in learning [21].Receiving piano music education can make people have a wonderful experience through unique musical melody, rhythm, as well as colorful sounds. It can also stimulate students’ rich imagination and rich emotions. Therefore, in order to enable students to move, student interaction in the assessment class is important [22]. It can arouse the emotions of students to participate in all educational multimedia technology tools that are important means to mobilize students’ learning emotions [23]. Knowing some piano music knowledge is helpful for students to move to work in the future. Generally, students who often participate in piano music activities in school have better adaptability to work after graduation than students who do not like piano music. They have a positive work attitude and are easy to communicate with colleagues due to their personality, which will also affect their work performance [24]. Therefore, understanding a certain piano music knowledge enables students to have more sense of superiority at work and enhance their self-confidence.


### 2.2. Shared Classroom Construction

Traditional remote online teaching is basically a non-real-time on-demand mode of video and audio on demand, lacking on-site effects, and instant interaction, while remote real-time classrooms solve the above problems well, allowing remote online students to experience real and vivid teaching methods [25]. At the same time, remote real-time classrooms have strong class authenticity and interactive participation It eliminates the learning inertia of radio listening and video-on-demand, allowing students to have an atmosphere of “learning” and “in class.” Online students can follow the classroom and online students through high-quality audio and video just like in a real classroom. Students in an online classroom can engage in interactive discussions with teachers through high-quality audio and video, just as they would in a real church [26].In the remote real-time classroom sharing system, two-way video sharing and interaction is the key. Only through high-quality video sharing and interaction can the teaching situation in the classroom be truly transmitted to other online classes and all individual learners [27]. At the same time, the videos of all learners participating in the classroom are shared with teachers and other users, so that all group learning and individual learning can have an immersive feeling and accept a certain degree of learning status supervision, which can solve the free and uncontrollable behavior of online learners in the traditional webcast classroom. The traditional webcast classroom is mostly one-way video interaction. Teachers transmit videos to online learners, but there is no video interaction between online learners, teachers, and other online learners. Teachers know nothing about the class situation of online learners. To the greatest extent, they can only make rough data statistics through the login times and login time of the software system, which cannot guarantee the learning quality of online learners [28, 29].For shared classrooms, only the video signal of itself and the online classroom will be turned on. In this way, at least there is a high-quality video browsing request service. The back-end server has to receive the video data transmitted by each client and respond to others. The client requests browsing and access to the video data, so even a server with a high configuration cannot withstand such a huge service request [30]. Therefore, the performance and processing capacity of the background server have become the biggest performance bottlenecks in the entire remote real-time classroom sharing system. How to solve this problem has become the key to the large-scale application of the remote real-time classroom sharing system.In order to solve the above-mentioned performance bottlenecks, server load balancing technology can be used on the back-end server side of the remote real-time classroom sharing system. Multiple servers form a server cluster to provide video services, and the video pool is evenly distributed to each server according to certain rules. For the above case, if a server is used in the back end, under normal circumstances, on average, each service only needs to process high-quality video signal reception and high-quality video browsing request services, so that the load of the server can be reduced to one. The acceptable range provides a stable background service platform guarantee for the remote real-time classroom sharing system. The schematic diagram of load balancing technology is shown in Figure1.Figure 1
Schematic diagram of load distribution under load balancing.


### 2.3. Load Balancing Technology

A server cluster connects many homogeneous or heterogeneous servers through the network to form a distributed system. These servers provide a unified calling interface to the outside, receive input and return results. Internally, tasks are decomposed and distributed calculations are carried out to speed up the speed. The key to load balancing clusters is to distribute tasks to each server as evenly as possible, avoiding some servers being overloaded and some servers being idle [31].The load balancing device acts as a gateway, intercepts user requests in advance, and then distributes them to the back-end server that really handles user requests according to a load balancing strategy. Therefore, the service request message sent by the client must first be sent to the IP address of the load balancing device. Because this address is not responsible for processing the actual service operation, this address is usually called the virtual IP address, and the real service server at the back end is called the real server. The load balancing device communicates with the client through the virtual IP address and then reasonably distributes the load to the real server. Its principle is shown in Figure2.Figure 2
Principles of load balancing technology.Load balancing equipment is being developed in the smart DNS system. When a user sends any access request, it must pass through the DNS smart system first, and then GSLB decides to distribute the user request to the IP address of the best server. K-means is the most classic division method. Given a set of data points and the required number of clustersk, k is specified by the user, and the k-means algorithm repeatedly divides the data into k clusters according to a certain distance function. The Euclidean distance (Xi, Xj) of two objects in objects is as follows:(1)dxi,xj=xi1−xj12+xi2−xj22+⋯+xin−xjn2,with P-dimensional attributes.In the basic definition of K-means algorithm,(2)Wi=1ni∑ii1,1ni∑ii2,…1ni∑iin.Among them,(3)inner=1n∑I∈cii1−wi12,where inner is a data object with dimension p in the cluster. Of the similarity between clusters is as follows:(4)ext=minwi−wj=minwi1−wj12.Among them,wi and wj are the centroids of cluster Ci and Cj:(5)v1=1s1∑s1∈x1xk,i=1,…,c,(6)minzU=∑i=1c∑k=1mμkxk−vk2.K-means cluster analysis method can be used as follows:(7)Mfc=∑k=1nμkxk+∑k=1nμj,(8)dk=∑j=1nx−kjvk21/2,(9)W=∑k=1n∑v=1pηkdvc2,(10)v1=∑k=1nμikmXk⋅∑k=1nμkU∈Mfc,(11)xk−v1G2=xk−viTGxk−v1,(12)xk−viG2=xk−v12+∑n=1i=2,(13)minzU,V=∑i=1c∑k=1nμikmxk−v12.Min expresses the uncertainty and concern in the collection of information and descriptions.For the clusterk∗l−1,(14)bij′=1,ifbij≤bij+1,−1,ifbij≥bij+1,0,ifbij=bij+1,(15)N=maxi′∈I,i′≠iN,Otherwise,δx=0.(16)maxN=∑j∈Jδbij′=bi′j′,(17)CSIi1,i2,i3=∑j∈Jδb′i1j=b′i2j=b′i3jMaxN,(18)Zj=BTx−ijd∈Rm.Later,(19)Ev,h|θ=−∑i=1naivi−∑j=1mbjhj,where bj represents bias of the hidden layer unit, and Wij represents connection weight between the visible layer and the hidden layer.In the distance-based outlier detection algorithm, when the object dataN and other objects, the only distance between the object and the setting object to be exported must be calculated, which can prevent the calculated object from all other objects (A or B). An error occurred in the distance between the original data system.(20)n≥fn+Nvlog1δ+Nvlog1∂2+2fvlog1ε.Evaluation indexE will become get better and better. Finally, the shared classroom structure constructed according to the algorithm is shown in Figure 3.Figure 3
Shared classroom structure.


## 2.1. Multimedia

Social psychology believes that facial expressions are an important mode of interpersonal communication, and they contribute the most to emotional expression. Researchers use cameras to obtain facial images with facial expression information, extract facial expression features from them, and use them to identify human emotional states. Many researchers obtain expression features by transforming the whole or part of the image. Commonly used image transformation methods include discrete cosine transform, Haar transform, and so on. In order to accurately extract facial expression features. The AdaBoost algorithm is a fast algorithm that can be used for face detection. Facial expression image databases commonly used in experiments include JAFFE and so on. JAFFE is the database of Japanese female facial expressions. There are 213 facial expressions in the database, consisting of seven facial expressions of 10 women.Education requires not only a solid cultural literacy but also the mastery of various quality abilities [13, 14]. To vigorously develop quality education is inseparable from the use. In the teaching of piano music appreciation class, it is necessary to learn to rely on modern multimedia technology to guide students to experience, appreciate, and understand piano music in various ways, to stimulate students’ musical imagination and creativity, as well as their love for piano music learning [15].Change people’s living standards has also become a necessary way for people’s spiritual and cultural progress [16]. Piano music forms as art show a distinction between good and bad, and students have poor ability to discriminate piano music due to age, psychological, and cognitive constraints and are easily affected by the environment. Many excellent piano music works are used by students. Think it is not good, but popular songs and pop piano music are more popular with students. Under such circumstances, piano music teachers cultivate students’ love for excellent works. Therefore, improving the requirements for students, strengthening the guidance of students, and cultivating students’ good ability to appreciate piano music have also become important part of piano music lessons. Enthusiasm for learning allows students to understand classic piano music works more vividly, instead of learning with a feeling of rejection [17]. Multimedia technology in piano music appreciation classes has become an irreversible trend of the times.Multimedia uses text, photos, images, audio, video, courses, and other formats to simulate situations [18]. In the simulated situation, students can enter their own environment to complete the evaluation and analysis of musical works [19, 20]. Teachers’ preaching and repeated supervision are not as good as students’ interest, which interested in use multimedia technology to create piano music situations in the teaching process, download images, videos, and other content-related materials to pay attention to create a situation that is conducive to students’ learning can greatly interest in learning [21].Receiving piano music education can make people have a wonderful experience through unique musical melody, rhythm, as well as colorful sounds. It can also stimulate students’ rich imagination and rich emotions. Therefore, in order to enable students to move, student interaction in the assessment class is important [22]. It can arouse the emotions of students to participate in all educational multimedia technology tools that are important means to mobilize students’ learning emotions [23]. Knowing some piano music knowledge is helpful for students to move to work in the future. Generally, students who often participate in piano music activities in school have better adaptability to work after graduation than students who do not like piano music. They have a positive work attitude and are easy to communicate with colleagues due to their personality, which will also affect their work performance [24]. Therefore, understanding a certain piano music knowledge enables students to have more sense of superiority at work and enhance their self-confidence.


## 2.2. Shared Classroom Construction

Traditional remote online teaching is basically a non-real-time on-demand mode of video and audio on demand, lacking on-site effects, and instant interaction, while remote real-time classrooms solve the above problems well, allowing remote online students to experience real and vivid teaching methods [25]. At the same time, remote real-time classrooms have strong class authenticity and interactive participation It eliminates the learning inertia of radio listening and video-on-demand, allowing students to have an atmosphere of “learning” and “in class.” Online students can follow the classroom and online students through high-quality audio and video just like in a real classroom. Students in an online classroom can engage in interactive discussions with teachers through high-quality audio and video, just as they would in a real church [26].In the remote real-time classroom sharing system, two-way video sharing and interaction is the key. Only through high-quality video sharing and interaction can the teaching situation in the classroom be truly transmitted to other online classes and all individual learners [27]. At the same time, the videos of all learners participating in the classroom are shared with teachers and other users, so that all group learning and individual learning can have an immersive feeling and accept a certain degree of learning status supervision, which can solve the free and uncontrollable behavior of online learners in the traditional webcast classroom. The traditional webcast classroom is mostly one-way video interaction. Teachers transmit videos to online learners, but there is no video interaction between online learners, teachers, and other online learners. Teachers know nothing about the class situation of online learners. To the greatest extent, they can only make rough data statistics through the login times and login time of the software system, which cannot guarantee the learning quality of online learners [28, 29].For shared classrooms, only the video signal of itself and the online classroom will be turned on. In this way, at least there is a high-quality video browsing request service. The back-end server has to receive the video data transmitted by each client and respond to others. The client requests browsing and access to the video data, so even a server with a high configuration cannot withstand such a huge service request [30]. Therefore, the performance and processing capacity of the background server have become the biggest performance bottlenecks in the entire remote real-time classroom sharing system. How to solve this problem has become the key to the large-scale application of the remote real-time classroom sharing system.In order to solve the above-mentioned performance bottlenecks, server load balancing technology can be used on the back-end server side of the remote real-time classroom sharing system. Multiple servers form a server cluster to provide video services, and the video pool is evenly distributed to each server according to certain rules. For the above case, if a server is used in the back end, under normal circumstances, on average, each service only needs to process high-quality video signal reception and high-quality video browsing request services, so that the load of the server can be reduced to one. The acceptable range provides a stable background service platform guarantee for the remote real-time classroom sharing system. The schematic diagram of load balancing technology is shown in Figure1.Figure 1
Schematic diagram of load distribution under load balancing.


## 2.3. Load Balancing Technology

A server cluster connects many homogeneous or heterogeneous servers through the network to form a distributed system. These servers provide a unified calling interface to the outside, receive input and return results. Internally, tasks are decomposed and distributed calculations are carried out to speed up the speed. The key to load balancing clusters is to distribute tasks to each server as evenly as possible, avoiding some servers being overloaded and some servers being idle [31].The load balancing device acts as a gateway, intercepts user requests in advance, and then distributes them to the back-end server that really handles user requests according to a load balancing strategy. Therefore, the service request message sent by the client must first be sent to the IP address of the load balancing device. Because this address is not responsible for processing the actual service operation, this address is usually called the virtual IP address, and the real service server at the back end is called the real server. The load balancing device communicates with the client through the virtual IP address and then reasonably distributes the load to the real server. Its principle is shown in Figure2.Figure 2
Principles of load balancing technology.Load balancing equipment is being developed in the smart DNS system. When a user sends any access request, it must pass through the DNS smart system first, and then GSLB decides to distribute the user request to the IP address of the best server. K-means is the most classic division method. Given a set of data points and the required number of clustersk, k is specified by the user, and the k-means algorithm repeatedly divides the data into k clusters according to a certain distance function. The Euclidean distance (Xi, Xj) of two objects in objects is as follows:(1)dxi,xj=xi1−xj12+xi2−xj22+⋯+xin−xjn2,with P-dimensional attributes.In the basic definition of K-means algorithm,(2)Wi=1ni∑ii1,1ni∑ii2,…1ni∑iin.Among them,(3)inner=1n∑I∈cii1−wi12,where inner is a data object with dimension p in the cluster. Of the similarity between clusters is as follows:(4)ext=minwi−wj=minwi1−wj12.Among them,wi and wj are the centroids of cluster Ci and Cj:(5)v1=1s1∑s1∈x1xk,i=1,…,c,(6)minzU=∑i=1c∑k=1mμkxk−vk2.K-means cluster analysis method can be used as follows:(7)Mfc=∑k=1nμkxk+∑k=1nμj,(8)dk=∑j=1nx−kjvk21/2,(9)W=∑k=1n∑v=1pηkdvc2,(10)v1=∑k=1nμikmXk⋅∑k=1nμkU∈Mfc,(11)xk−v1G2=xk−viTGxk−v1,(12)xk−viG2=xk−v12+∑n=1i=2,(13)minzU,V=∑i=1c∑k=1nμikmxk−v12.Min expresses the uncertainty and concern in the collection of information and descriptions.For the clusterk∗l−1,(14)bij′=1,ifbij≤bij+1,−1,ifbij≥bij+1,0,ifbij=bij+1,(15)N=maxi′∈I,i′≠iN,Otherwise,δx=0.(16)maxN=∑j∈Jδbij′=bi′j′,(17)CSIi1,i2,i3=∑j∈Jδb′i1j=b′i2j=b′i3jMaxN,(18)Zj=BTx−ijd∈Rm.Later,(19)Ev,h|θ=−∑i=1naivi−∑j=1mbjhj,where bj represents bias of the hidden layer unit, and Wij represents connection weight between the visible layer and the hidden layer.In the distance-based outlier detection algorithm, when the object dataN and other objects, the only distance between the object and the setting object to be exported must be calculated, which can prevent the calculated object from all other objects (A or B). An error occurred in the distance between the original data system.(20)n≥fn+Nvlog1δ+Nvlog1∂2+2fvlog1ε.Evaluation indexE will become get better and better. Finally, the shared classroom structure constructed according to the algorithm is shown in Figure 3.Figure 3
Shared classroom structure.


## 3. Sharing Classroom Piano Music Education Experiments and Results

### 3.1. Subjects

Before conducting multimedia network sharing courses, we need to test the multimedia network environment. The test environment constructed in this paper is shown in Table1.Table 1
Test environment computer configuration.
Configuration of the test environment computerHardwareCPUIntel(R) Core(TM)i5-65003.20 GHzRAM8.00 GBSoftwareHard disk500 GBOperating systemMicrosoft Windows 7BrowserInternet Explorer 8.0We conduct a survey of our school’s students and teachers. Our school is a collection of junior high and high school learning. We collect statistics on these students and piano music teachers. The teacher’s teaching age is shown in Table2:Table 2
Teaching years of teachers.
Less than 1 year1–5 years5–10 yearsMore than 10 yearsJunior high school1241High school0471Percentage5%30%55%10%Twenty teachers from junior high school and senior high school are selected as the subjects of this experiment, most of whom have rich teaching experience, and the proportion of teaching age of 5–10 years is as high as 55%, indicating that the teacher experience of this teaching experiment can meet the standard. We make statistics on the piano music teaching methods conducted by teachers before, and the results are shown in Table3:Table 3
Teaching methods used.
Teaching fan singingSituational inductionDiscussion methodTask-driven approachPiano music gameCompetition andcooperation actJunior high school1057101High school1045012Percentage100%45%60%5%5%15%In the teaching between teachers, the teaching method used by teachers is teaching method, which is also the most basic teaching method in piano music class. In addition, each teacher has its own teaching method in the teaching process. Among them, situational simulation and student discussion are the teaching methods, and a small number of teachers use piano music games and competitive cooperation methods. It has also achieved certain results, but from the perspective of the overall method, the traditional teaching methods are mostly confined to offline teaching and rarely involve multimedia and online teaching, which is difficult to meet today’s continuous.We divided the junior high and high school students into traditional teaching and shared classroom teaching groups by grouping the students in the middle and high schools of our school. After 3 months of teaching, we compared them. First, we compared the piano music of the students before teaching. The accomplishment is counted. We set the full score as 1, and the results of the various musical accomplishments of the students are shown in Table4:Table 4
Students’ piano music accomplishment.
IntonationTimbreCollaborationLearning abilityInterestFirst grade0.230.190.170.240.48Second grade0.250.290.250.210.38Third grade0.210.230.200.180.30Senior first0.380.390.360.350.27Senior second0.240.210.220.240.24Senior three0.190.220.230.210.20Without the money for piano music teaching, students’ understanding of piano music is basically the same. Piano music interest is in a downward trend.


### 3.2. Comparison before and after Teaching

Musical instruments are divided into Western musical instruments and traditional musical instruments. They are roughly shown in Figure4. We have collected some long-used musical instruments, but due to time and cost constraints, all our experiments in this teaching only compare piano teaching.Figure 4
Part of the collection of musical instruments.In order to collect the difference between the shared classroom and the traditional teaching mode, we collected the timbre, intonation, cooperative sharing, learning ability, and interest of different groups of students. We first compared the difference in tone of different groups before and after teaching. As shown in Figure5, where Figure 5(a) is the comparison of junior high school, and Figure 5(b) is the comparison of high school.Figure 5
Tone comparison results.
(a)(b)After the division teaching of middle and high schools, the students in the two groups have improved to a certain extent in terms of timbre, but overall, in the teaching method of using multimedia sharing classrooms, the extent of improvement is to go far beyond traditional teaching methods, the average value of junior high school classes has increased from about 0.2 to 0.45, and the average value of high school classes has increased from 0.25 to about 0.48. From the comparison of classes, the improvement of high school classes is slightly better than that of junior high school classes.We made statistics on the intonation of the class and compared the changes before and after. The results are shown in Figure6, where Figure 6(a) is the comparison of junior high school, and Figure 6(b) is the comparison of high school.Figure 6
Intonation comparison results.
(a)(b)In the tone color comparison, the comparison different grades are great. In the junior high school class, after the shared classroom and methods, the difference in intonation between the two groups of students is not big. The gap is around 15%. In the high school class, the contrast and the score of the multimedia sharing classroom is about 40% higher than that of the traditional teaching.We made statistics on the awareness of cooperation and sharing among students, and the comparison results are shown in Figure7, where Figure 7(a) is the comparison of junior high school, and Figure 7(b) is the comparison of high school:Figure 7
Comparison of cooperation and sharing awareness.
(a)(b)It can be seen from the figure that in terms of cooperative and sharing awareness, the average value of high school classes is higher than that of junior high school classes. This may be due to the fact that know how to cooperate and share better. However, from the effect of methods, the sharing consciousness after multimedia sharing classroom teaching is also higher than traditional teaching methods, which also shows that multimedia sharing classrooms.In order to make a more detailed comparison, we selected four-time nodes for the 3-month teaching and made statistics on the learning ability and students’ interest data. The results are shown in Figures8 and 9:Figure 8
Comparison of learning ability effects. (a) 7-day comparison, (b) 15-day comparison, (c) 1-month comparison, and (d) 3-month comparison.
(a)(b)(c)(d)Figure 9
Students interested in changes. (a) 7-day comparison, (b) 15-day comparison, (c) 1-month comparison, and (d) 3-month comparison.
(a)(b)(c)(d)The changes of students’ learning ability and interest scores are basically similar. Before half a month of teaching, the scores of traditional teaching and multimedia sharing teaching change little, and the overall score is close. Even traditional teaching occasionally takes the lead, but after more than half a month of teaching. The difference between the teaching effect of multimedia shared classroom and traditional teaching begins to widen, and the leading range can exceed 50%, which shows that multimedia shared classroom can achieve excellent results for piano music teaching.Of course, for piano music teaching, we should not only focus on students but also for teachers; we should also conduct certain statistics. Therefore, we have conducted a survey on the satisfaction of teachers after teaching. The results are shown in Figure10:Figure 10
Teacher satisfaction survey.Satisfaction of using multimedia sharing classrooms is extremely high, and the number of people who are satisfied or above can account for more than 65%, acceptance of multimedia sharing classrooms.


## 3.1. Subjects

Before conducting multimedia network sharing courses, we need to test the multimedia network environment. The test environment constructed in this paper is shown in Table1.Table 1
Test environment computer configuration.
Configuration of the test environment computerHardwareCPUIntel(R) Core(TM)i5-65003.20 GHzRAM8.00 GBSoftwareHard disk500 GBOperating systemMicrosoft Windows 7BrowserInternet Explorer 8.0We conduct a survey of our school’s students and teachers. Our school is a collection of junior high and high school learning. We collect statistics on these students and piano music teachers. The teacher’s teaching age is shown in Table2:Table 2
Teaching years of teachers.
Less than 1 year1–5 years5–10 yearsMore than 10 yearsJunior high school1241High school0471Percentage5%30%55%10%Twenty teachers from junior high school and senior high school are selected as the subjects of this experiment, most of whom have rich teaching experience, and the proportion of teaching age of 5–10 years is as high as 55%, indicating that the teacher experience of this teaching experiment can meet the standard. We make statistics on the piano music teaching methods conducted by teachers before, and the results are shown in Table3:Table 3
Teaching methods used.
Teaching fan singingSituational inductionDiscussion methodTask-driven approachPiano music gameCompetition andcooperation actJunior high school1057101High school1045012Percentage100%45%60%5%5%15%In the teaching between teachers, the teaching method used by teachers is teaching method, which is also the most basic teaching method in piano music class. In addition, each teacher has its own teaching method in the teaching process. Among them, situational simulation and student discussion are the teaching methods, and a small number of teachers use piano music games and competitive cooperation methods. It has also achieved certain results, but from the perspective of the overall method, the traditional teaching methods are mostly confined to offline teaching and rarely involve multimedia and online teaching, which is difficult to meet today’s continuous.We divided the junior high and high school students into traditional teaching and shared classroom teaching groups by grouping the students in the middle and high schools of our school. After 3 months of teaching, we compared them. First, we compared the piano music of the students before teaching. The accomplishment is counted. We set the full score as 1, and the results of the various musical accomplishments of the students are shown in Table4:Table 4
Students’ piano music accomplishment.
IntonationTimbreCollaborationLearning abilityInterestFirst grade0.230.190.170.240.48Second grade0.250.290.250.210.38Third grade0.210.230.200.180.30Senior first0.380.390.360.350.27Senior second0.240.210.220.240.24Senior three0.190.220.230.210.20Without the money for piano music teaching, students’ understanding of piano music is basically the same. Piano music interest is in a downward trend.


## 3.2. Comparison before and after Teaching

Musical instruments are divided into Western musical instruments and traditional musical instruments. They are roughly shown in Figure4. We have collected some long-used musical instruments, but due to time and cost constraints, all our experiments in this teaching only compare piano teaching.Figure 4
Part of the collection of musical instruments.In order to collect the difference between the shared classroom and the traditional teaching mode, we collected the timbre, intonation, cooperative sharing, learning ability, and interest of different groups of students. We first compared the difference in tone of different groups before and after teaching. As shown in Figure5, where Figure 5(a) is the comparison of junior high school, and Figure 5(b) is the comparison of high school.Figure 5
Tone comparison results.
(a)(b)After the division teaching of middle and high schools, the students in the two groups have improved to a certain extent in terms of timbre, but overall, in the teaching method of using multimedia sharing classrooms, the extent of improvement is to go far beyond traditional teaching methods, the average value of junior high school classes has increased from about 0.2 to 0.45, and the average value of high school classes has increased from 0.25 to about 0.48. From the comparison of classes, the improvement of high school classes is slightly better than that of junior high school classes.We made statistics on the intonation of the class and compared the changes before and after. The results are shown in Figure6, where Figure 6(a) is the comparison of junior high school, and Figure 6(b) is the comparison of high school.Figure 6
Intonation comparison results.
(a)(b)In the tone color comparison, the comparison different grades are great. In the junior high school class, after the shared classroom and methods, the difference in intonation between the two groups of students is not big. The gap is around 15%. In the high school class, the contrast and the score of the multimedia sharing classroom is about 40% higher than that of the traditional teaching.We made statistics on the awareness of cooperation and sharing among students, and the comparison results are shown in Figure7, where Figure 7(a) is the comparison of junior high school, and Figure 7(b) is the comparison of high school:Figure 7
Comparison of cooperation and sharing awareness.
(a)(b)It can be seen from the figure that in terms of cooperative and sharing awareness, the average value of high school classes is higher than that of junior high school classes. This may be due to the fact that know how to cooperate and share better. However, from the effect of methods, the sharing consciousness after multimedia sharing classroom teaching is also higher than traditional teaching methods, which also shows that multimedia sharing classrooms.In order to make a more detailed comparison, we selected four-time nodes for the 3-month teaching and made statistics on the learning ability and students’ interest data. The results are shown in Figures8 and 9:Figure 8
Comparison of learning ability effects. (a) 7-day comparison, (b) 15-day comparison, (c) 1-month comparison, and (d) 3-month comparison.
(a)(b)(c)(d)Figure 9
Students interested in changes. (a) 7-day comparison, (b) 15-day comparison, (c) 1-month comparison, and (d) 3-month comparison.
(a)(b)(c)(d)The changes of students’ learning ability and interest scores are basically similar. Before half a month of teaching, the scores of traditional teaching and multimedia sharing teaching change little, and the overall score is close. Even traditional teaching occasionally takes the lead, but after more than half a month of teaching. The difference between the teaching effect of multimedia shared classroom and traditional teaching begins to widen, and the leading range can exceed 50%, which shows that multimedia shared classroom can achieve excellent results for piano music teaching.Of course, for piano music teaching, we should not only focus on students but also for teachers; we should also conduct certain statistics. Therefore, we have conducted a survey on the satisfaction of teachers after teaching. The results are shown in Figure10:Figure 10
Teacher satisfaction survey.Satisfaction of using multimedia sharing classrooms is extremely high, and the number of people who are satisfied or above can account for more than 65%, acceptance of multimedia sharing classrooms.


## 4. Discussion

People express their emotions through facial expressions, voices, body movements, texts, and so on, so emotion recognition is essentially a multimodal fusion problem. Multimodal emotion recognition uses modal fusion technology to comprehensively utilize information from multiple information sources. There are three types of commonly used modal fusion technologies combines emotional features extracted from multiple modalities to construct a joint feature vector to identify emotional states. Since the time scale of each mode may be different, this method will increase the difficulty of synchronization; at the same time, the increased feature dimension will also reduce the time.The important task of education development is to implement the strategy of “rejuvenating the country through science and education” and the strategy of “rapid economic development.” In our piano music classroom, traditional piano music teaching methods can no longer raise students’ interest in learning, which not only limits the aesthetic function of piano music but also makes the classroom lack of vitality. Therefore, the application strategy of multimedia-assisted teaching in the piano music appreciation classroom has become important.Taking the open course of Chinese and foreign history piano music in colleges and universities as an example, this type of course is boring for most students. The reason why students have such an impression is also because our tradition mainly based on text consistency. The public piano music course in colleges is a popular course of piano music teaching in nonpiano music subjects. According to the particularity of the subject, the teaching content must be distinguished from that of professional piano music students. In addition, in terms of the nature of teaching, it is no longer a pure piano music appreciation course but should include a wider range of disciplines; develop a horizontal; and combine the knowledge of history, geography, and humanities. The teaching content has different requirements.In the face of higher teaching requirements, it is really inappropriate to use descriptive text and supplementary illustrative legends and diagrams in the past. Teaching content and teaching materials have more extensive search channels, and with the assistance of text, images, sound, video, animation and other media forms, it seems to be difficult. Diversified knowledge structure has become much easier here.By sharing the classroom teaching mode, students gradually master a certain knowledge of piano music and improve their ability to appreciate piano music. Through mutual help and cooperation between group members, the individual differences in the ability of students to appreciate piano music have been improved. Therefore, this article believes that the new teaching model has a certain effect on improving students’ piano music appreciation ability and alleviating individual differences in students’ piano music appreciation ability.


## 5. Conclusion

From facial expression image sequences, establish an detect and track the coordinates of facial feature points and extract the displacement. Each frame of facial expression images constitutes an expression feature vector sequence. This article combines the theoretical and practical achievements of predecessors on shared classrooms and the status quo of piano music appreciation teaching to construct a piano music appreciation shared classroom teaching model. Using the constructed model, after 3 months of action research, through the data statistics and analysis of action research, it is concluded that the shared classroom teaching model of piano music appreciation class helps to solve the individual differences in the ability and interest of students to appreciate piano music. The problems that help students develop their ability to perceive piano music can help solve the problems that arise in traditional piano music appreciation classes. Piano music appreciation shared classroom teaching model is loved by most teachers and students, which has favorable conditions for the further development of the shared classroom teaching model in the piano music subject. The article only counts the students of our school. The data sample is not large, and it may be interfered by the students’ original level, which may cause certain errors in the results. Therefore, increase the amount of sample data, increase the time span, reduce variables, and improve the credibility of the experiment.



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*Source: 1882739-2022-08-24.xml*