# Psychological Adjustment and Emotional Health Care Strategies in the Teaching Process of College Music Teachers Based on Big Data Analysis

Authors: Songqing Ma; Yuhe Liu
Journal: Mathematical Problems in Engineering
 (2022)

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## Abstract

In music teaching in high schools, teachers should not only impart corresponding music knowledge to students but also focus on cultivating students’ comprehensive quality and quality. In the process of music teaching in universities, we should focus on students’ psychological adjustment and emotional health care. In order to quantify the application effect of big data technology in music teaching reform, this paper takes piano teaching in universities as an example to conduct research and mainly analyzes the physiological and psychological prediction effects involved in the teaching process. This paper introduces three typical big data technologies to predict and analyze the data involved in the existing piano teaching in universities. The analysis results show that the prediction effect based on the fuzzy neural inference system is the best. In addition, the three-dimensional display of the prediction data shows that the prediction effect obtained by big data technology shows good consistency and continuity, which indicates that the prediction method based on big data technology is suitable for psychological adjustment and emotional health care in music teaching in universities.


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## Body

## 1. Introduction

Music, as an independent art, is aesthetic [1, 2]. In people’s aesthetic activities, the artistic image is born out of emotion, which makes the aesthetic subject feel the same, and evokes various emotional experiences of the appreciator. Studies [3, 4] have confirmed that the melody and timbre changes and rhythm and beat movements of music can radiate the unique charm of the human spiritual world. When it mobilizes various factors such as memory, association, and imagination of people’s thinking, it can arouse people’s sympathy and resonate. The study pointed out that music therapy is a method based on the theory of psychological diagnosis and treatment. It uses the physical and psychological reactions produced by music, with the help of music therapists, using established music interactions and then relying on the influence of music perception, so that patients can regain confidence and gain a healthy mind. As shown in Figure 1, in the current era, there are generally the following types of music therapy methods that have more appeal in the world: psychodynamic music therapy, cognitive music therapy, behavioral music therapy, guided imagery music therapy, etc. First of all, the concert brings the audience a happy mood, which is the process of psychodynamic therapy. Music can then change the perception and behavior of the audience through their characteristic melodies. Finally, through the above operations, music can make the audience have a reasonable imagination space.Figure 1
Components of college music therapy.In addition to these types of therapy, it also includes transpersonal music therapy and personality-oriented music therapy. These scientific music therapy methods can provide a model for college students’ mental health education as a reference.At present, in some developed countries abroad, music has achieved obvious therapeutic effects on the treatment of mental health diseases. Therefore, the subject exploration of music therapy abroad has developed into a practical subject with strong clinical application, that is, music therapy. Music therapy became an independent subject in universities and first appeared in the United States, so the United States also occupies a leading position in the global music therapy industry [5, 6]. Up to now, music therapy has been widely used in many countries around the world. According to statistics from the Global Music Therapy Federation, music therapy has been implemented in 45 countries in the world, and 150 universities have carried out music therapy teaching. In recent years, with the rapid development of China's social economy, the teaching mode and teaching methods and means of universities are constantly maturing. Therefore, music therapy education has gradually been incorporated into the teaching system and teaching management of Chinese universities. However, China is still in its infancy, mainly learning from the West, with less original research on its own. In the actual teaching work, music education in Chinese universities still faces a series of practical problems. Therefore, the effective implementation of music therapy in universities still needs continuous improvement.For the specific group of college students, how to combine the psychological characteristics of college students and integrate music therapy into the daily study and life of college students is a problem we should focus on.As we all know, music is a romantic word. As far as most people are concerned, the music teaching process in universities is more about singing and laughing, singing, and dancing, keeping away from worries and troubles and making people mentally healthier. However, on the contrary, according to the scientific research statistics in recent years, it is found that the mental health problems of music teachers in universities are relatively serious. At the same time, music teachers and musicians are groups with distinct industry characteristics and special psychological states. Therefore, how to understand the psychological activities and psychological states in the process of music teaching and how to break the bottleneck of mental health of music teachers in universities and further improve the quality of teaching in universities are issues worthy of consideration at this stage.In order to explore the real cause of this problem, it is necessary to deeply understand the psychological phenomena and psychological characteristics of music workers, especially music teachers who are engaged in music careers in universities. Musicians have their own unique psychological characteristics. First of all, as an art major, music has relatively high requirements on people’s image thinking ability, creativity, and overall sensitivity. Moreover, it will be honed in the teaching practice process. At the same time, the training of musical skills requires enduring loneliness alone. Workers in the music business require long hours of individual training and pondering every day. Therefore, the groups engaged in teaching and learning in this area are more critical. In addition, musicians tend to be simpler and more easily injured because they have less contact with people. Moreover, their character appears to be aloof, arrogant, and self-conscious.In addition, the music group has a strong sense of musical hearing, explicitness, and expressiveness required by music. At the same time, the direct communication characteristics of music, especially professional skills, such as instrumental music, dance, vocal music, and conducting, will require stage performance and emotional appeal. This will naturally lead to differences in the overall character and psychological process of music lovers from other art groups. However, these differences often manifest as excitability, emotionality, and high ups and downs. Therefore, their overall performance is more extroverted.In the fusion of these talents and professional characteristics, teachers and students have a lot of emotional ups and downs in the classroom. When the class is exciting, everyone is enthusiastic and impulsive, even dancing, and the music and singing are very loud. However, when there is no feeling in the classroom, teachers and students may be free and loose, lacking the basic process and discipline of regular teaching. In terms of music teaching methods, due to the characteristics of visualization and creative thinking, teachers prefer to use perceptual methods such as metaphors, inspiration, and feelings. Moreover, each teacher will have a relatively distinct teaching image thinking and language system, and it is easy to seek changeable and new forms of expression. This requires students to learn more by relying on comprehension. However, once you cannot adapt to this learning rhythm, it becomes difficult.


## 2. Psychological Adjustment and Mental Health in the Teaching of Music Major in Universities

In the teaching process, according to the unique psychological characteristics of music groups, as a music teacher, we should strive to achieve psychological stability and emotional peace for ourselves and students. On the one hand, teachers engaged in music teaching should change their cognition, accepting the setbacks and twists and turns in teaching are in line with the natural law of the development of things, rather than excessively pursuing extreme effects. When facing students with low talent or when teachers and students do not cooperate well in class, they should pay attention to each other’s emotions, so as not to affect teaching due to temporary emotions or even lead to students’ cognitive errors. On the other hand, in order to effectively improve the literacy of educational psychology, teachers and students should read more books on pedagogy and psychology and participate in courses and lectures in this area. Only in this way, can we be more sensitive to the ability to perceive and respond to their own psychological conditions in the teaching process.At the same time, in order to achieve the goal of psychological and emotional health care [7, 8], professional music teachers should not only pay attention to psychological and emotional adjustment in the process of music teaching but also consider the impact of psychological health outside the process of music teaching in universities on teaching. Music education is the inheritance education of the highest ideology of human spiritual culture, and it is the most essential and indispensable field of human civilization. The physical and mental health of teachers in music education in universities should be paid attention to. This requires us to improve the survival and growth conditions of music teachers in universities, introduce high-level psychological assistance programs, broaden the psychological knowledge of music teachers, make more use of psychological methods for self-care, and improve teaching efficiency. This is an important way to improve music education in universities in the future.It must be admitted that music appreciation is also an emotional activity, which contains distinct psychological factors. Music aesthetic psychology includes the listener’s music perception, aesthetic attitude, emotional experience, basic cognition, and associative imagination. Specifically, music appreciation is a highly subjective activity, which requires listeners to generate psychological emotional resonance from the perspective of hearing and further stimulate their own associative ability, so as to realize the appreciation of the essence of music. The aesthetic psychology of music includes aesthetic psychological factors such as musical perception, imaginative association, intuition, and insight. These factors are closely related to the acquisition of the appreciator’s aesthetic experience and reflect the essential laws of music everywhere. They are interactive and inseparable, which is a dynamic process of emotional fluctuations.In the specific teaching process in the initial stage of music appreciation, teachers need to mobilize students’ aesthetic attention and arouse their aesthetic expectations. In the specific teaching process in the initial stage of music appreciation, teachers need to mobilize students’ aesthetic attention and arouse their aesthetic expectations. In order to achieve the psychological adjustment and emotional health care of music teaching, teachers engaged in music education need to have the following professional skills or professional qualities.First, in music teaching, teachers should focus on creating teaching situations to stimulate students’ interest in music learning. Music teaching in universities should change the single and boring form of music teaching in the past. Teachers should focus on setting up corresponding teaching situations. They can strengthen students’ audio-visual experience through PPT, microlectures, short videos, etc., enrich students’ appreciation levels, and help students acquire different forms of musical beauty. While maintaining students’ interest in music learning, music teaching should focus on letting them express their own feelings and comprehension and stimulate students’ creative inspiration. Only in this way can students fully display their talents in the process of interaction with teachers.Second, in music teaching, teachers should continuously improve educational methods so that students can improve their aesthetic ability in practice. Teachers should return the dominant position of learning to students. This requires adding some interactive links in the classroom and allowing students to learn from each other’s skills. Teachers should return the dominant position of learning to students. This requires adding some interactive links in the classroom and allowing students to learn from each other’s skills. In addition, universities can hold more concerts and famous music performances for teachers and students to observe. In the process of appreciating everyone’s demeanor, students can accumulate music perception ability and professional theoretical knowledge.Third, in music teaching, teachers should improve their ability to appreciate music aesthetics and guide students scientifically and effectively. Teachers are the main guides of teaching activities, and they should have high musical literacy and aesthetic ability. Based on this, music teachers in universities should regularly participate in teaching seminars and business training, appreciate world famous songs, and broaden their horizons and minds, so as to better guide students in music learning and establish a good cultural connotation and music professional quality. Universities should conduct performance appraisals on teachers and urge teachers to improve their teaching ability in order to make more outstanding achievements in educational positions.With the third wave of artificial intelligence sweeping the world, artificial intelligence has once again become the focus of the whole society [9, 10]. With the third wave of artificial intelligence sweeping the world, artificial intelligence has once again become the focus of the whole society. With the advent of the era of big data, various application fields are inseparable from the assistance and blessing of artificial intelligence technology. In particular, big data technology [11, 12] has the unique advantages of not being limited by time and space, fast copying and dissemination, and rich in presentation effects. Therefore, the improvement method based on big data technology has important research significance. As we all know, big data technology is actually a special form of artificial intelligence, that is to say, big data technology is included in the link of artificial intelligence. The difference between the two is that big data technology is based on huge data sources to carry out prediction research.However, the application of big data technology enables a quantitative description, which is the elaboration of specific solutions to specific problems. In order to quantify the impact of big data technology on college music teaching, this paper introduces a specific example in college music teaching for quantitative research. College piano teaching is an important part of college music teaching. However, there are still many problems in the current stage of piano teaching in universities. These problems are mainly manifested in the strong randomness of curriculum education, the backward teaching mode, and the differences in students’ cognitive ability. In the piano teaching of universities, the law of playing breathing has the functions of making the player’s mood more stable, improving the coordination of the strength of various parts of the body, improving the level of the work, and improving the image of the music work. For this reason, based on the perspective of big data, this paper conducts relevant research on the laws of performance breathing in piano teaching in universities. For ordinary piano lovers, the breathing changes during the performance directly affect the performance. Therefore, based on the processing angle of big data, the variation law of breathing vibration during piano training is studied in detail. Specifically, by introducing several commonly used big data technology, this paper conducts a prediction study based on the corresponding value of the breathing law of performance in piano teaching, in order to provide some theoretical suggestions for psychological adjustment and emotional health care solutions in music teaching in universities.


## 3. Methods of Big Data Analysis

### 3.1. Multidimensional Support Vector Prediction

Extending the one-dimensional insensitive loss function to a multidimensional space, the loss function is defined, and the expression of the loss function is [13, 14](1)Lui=0,ui<ε,ui−ε2,ui≥ε,where ui=ei=eiTei; eiT=yiT−ϕTxiW−bt; W=w1,…,wQ; and b=b1,…,bQT, where ϕ is the nonlinear mapping kernel function; xi is the sample input row vector; yi is the sample output row vector; i=1,…,n, n is the number of samples; and Q is the dimensionality of the output variable.Based on the loss function shown in the above equation, we construct the optimization objective function with the expression:(2)LPW,b=12∑j=1Qωj2+C∑i=1nLui.In the formula,W and b represent the independent variables related to the support vector machine, respectively.To solve the mathematical optimization problem of the multidimensional output support vector regression model, this paper introduces the use of iterative reweighted least squares (IRSL) to solve the problem.In the optimization objective function of (2), the loss function is approximated by replacing it with a first-order Taylor expansion:(3)LP′W,b=12∑j=1Qωj2+C∑i=1nLuik+dLuidui|uikeikTuikei−eik.Constructing a quadratic approximation of (3) instead, the approximation formula we use expresses the following relation.(4)LP″W,b=12∑j=1Qωj2+C∑i=1nLuik+dLuidui|uikui2−uik22uik=12∑j=1Qωj2+12∑i=1naiui2+CT.The reason for using this approximation formula is thatW and b are decoupled in this formula, the optimization solution does not need to be iterated, and the approximate solutions of W and b can be calculated by taking the partial derivatives of W and b equal to 0 directly. The optimization objective is solved to obtain W and b that minimize the overall loss of the sample set, and the multioutput support vector regression model is established.In (4), an engineering parameter can be expressed as follows:(5)ai=CuikdLuidui|ui∗=0,uik<ε,2Cuik−εuik,uik≥ε.CT is a constant term that does not depend on W and b.In the research process, the objective function can be introduced into the generalized Lagrange multiplier. At this time, the optimization problem can be re-expressed as follows.(6)max∑i=1nαi−12∑i=1n∑j=1nαiαjyiyjkxi,xj,where αi is the Lagrangian coefficient. Then, the final optimized hyperplane can be expressed as follows.(7)fx=∑i=1nαiyikxi,xj+b,i=1,2,…,N.


### 3.2. Prediction Based on the Fuzzy Neural Inference System

The neural reasoning system is a system composed of three components, which mainly include (1) rule base, (2) database, and (3) reasoning system. In the fuzzy neural inference system, the input parameters consider different fuzzification and defuzzification methods and strategies and have various rules. This intelligent algorithm can choose from many sets of member functions to ensure the effect of fuzzy logic on the input data. The fuzzy inference system can be divided into three inference modes according to the “if-then rule” inference operation. These inference modes are Mamdani system, Sugeno system, and Tsukamoto system, respectively. Sugeno system is considered to be the most popular candidate for sample-based fuzzy modeling and facilitates the use of adaptive techniques. In a one-dimensional Sugeno system, a typical rule set with two computational rules for fuzzy inference can be expressed as follows [15, 16].Fuzzy comprehensive evaluation is carried out from the second level of factors. We can set the evaluation object to be the factor UIJ in the second level, the membership degree of the kth element in the evaluation set to be rijk, and then the single-factor membership degree matrix of the second level is(8)Ri=ri11ri12Lri1pri21ri22Lri2pMMMrin1rin2Lrinp.Then, the first-level fuzzy comprehensive evaluation model can be expressed as follows:(9)R=B1B2MBm=r11r12Lr1pr21r22Lr2pMMMrm1rm2Lrmp.


### 3.3. Elman Neural Network

tElman neural network is widely used for its large-scale parallel distributed structure and learning ability and generalization ability. The main advantages include nonlinear analysis capability, convenient input/output mapping, adaptive capability, evidence response, background information, strong fault tolerance, and VLSI (very large scale integrated) implementation. The key to the nonlinear ability and learning ability of neural network lies in the continuous modification of weights. There are two methods for recurrent network training, one is batch mode and the other is online mode. Like the BP neural network, the Elman neural network uses the ordered chain rule for learning and derivation. The output layer weights can be expressed as follows [17, 18].For the input layer, the Elman network can be represented as follows:(10)xi0=xik.For the hidden layer, the Elman network can be expressed as follows:(11)si1=∑j=1n0wij0xj0k+∑j=1n1wij2cj0k,xi1=f1si1k.For the association layer, the Elman network can be expressed as follows:(12)si2k=xi1k−1,cik=si2k.For the output layer, the Elman network can be represented as follows:(13)si3k=∑j=1n1wij1xj1k,yik=f2si3k,−∂Ek∂wij1=−∂Ek∂yik·∂yik∂wij1=eik·f2′si3k·xj1k.Similarly, the implicit value in the network can be expressed as follows.(14)−∂Ek∂wij0=−∑l=1r∂Ek∂ylk·∂ylk∂wij0=∑l=1relk·f2′sl3k·wij1k·∂xi1k∂wij0.Through comprehensive calculation, we can get(15)−∂Ek∂wij0=∑l=1relk·f2′sl3k·wij1k·χijik,χijik=f1′si1k·xj0k+∑m=1n1wim2·χijmk−1.In the actual operation process, the neural network algorithm can be used to replace the traditional BP neural network for research. At the same time, the correlation and difference between the two can also be reflected in the prediction process. Figure2 is a flow chart of the calculation process of the neural network.Figure 2
The calculation flow chart of the Elman neural network.


## 3.1. Multidimensional Support Vector Prediction

Extending the one-dimensional insensitive loss function to a multidimensional space, the loss function is defined, and the expression of the loss function is [13, 14](1)Lui=0,ui<ε,ui−ε2,ui≥ε,where ui=ei=eiTei; eiT=yiT−ϕTxiW−bt; W=w1,…,wQ; and b=b1,…,bQT, where ϕ is the nonlinear mapping kernel function; xi is the sample input row vector; yi is the sample output row vector; i=1,…,n, n is the number of samples; and Q is the dimensionality of the output variable.Based on the loss function shown in the above equation, we construct the optimization objective function with the expression:(2)LPW,b=12∑j=1Qωj2+C∑i=1nLui.In the formula,W and b represent the independent variables related to the support vector machine, respectively.To solve the mathematical optimization problem of the multidimensional output support vector regression model, this paper introduces the use of iterative reweighted least squares (IRSL) to solve the problem.In the optimization objective function of (2), the loss function is approximated by replacing it with a first-order Taylor expansion:(3)LP′W,b=12∑j=1Qωj2+C∑i=1nLuik+dLuidui|uikeikTuikei−eik.Constructing a quadratic approximation of (3) instead, the approximation formula we use expresses the following relation.(4)LP″W,b=12∑j=1Qωj2+C∑i=1nLuik+dLuidui|uikui2−uik22uik=12∑j=1Qωj2+12∑i=1naiui2+CT.The reason for using this approximation formula is thatW and b are decoupled in this formula, the optimization solution does not need to be iterated, and the approximate solutions of W and b can be calculated by taking the partial derivatives of W and b equal to 0 directly. The optimization objective is solved to obtain W and b that minimize the overall loss of the sample set, and the multioutput support vector regression model is established.In (4), an engineering parameter can be expressed as follows:(5)ai=CuikdLuidui|ui∗=0,uik<ε,2Cuik−εuik,uik≥ε.CT is a constant term that does not depend on W and b.In the research process, the objective function can be introduced into the generalized Lagrange multiplier. At this time, the optimization problem can be re-expressed as follows.(6)max∑i=1nαi−12∑i=1n∑j=1nαiαjyiyjkxi,xj,where αi is the Lagrangian coefficient. Then, the final optimized hyperplane can be expressed as follows.(7)fx=∑i=1nαiyikxi,xj+b,i=1,2,…,N.


## 3.2. Prediction Based on the Fuzzy Neural Inference System

The neural reasoning system is a system composed of three components, which mainly include (1) rule base, (2) database, and (3) reasoning system. In the fuzzy neural inference system, the input parameters consider different fuzzification and defuzzification methods and strategies and have various rules. This intelligent algorithm can choose from many sets of member functions to ensure the effect of fuzzy logic on the input data. The fuzzy inference system can be divided into three inference modes according to the “if-then rule” inference operation. These inference modes are Mamdani system, Sugeno system, and Tsukamoto system, respectively. Sugeno system is considered to be the most popular candidate for sample-based fuzzy modeling and facilitates the use of adaptive techniques. In a one-dimensional Sugeno system, a typical rule set with two computational rules for fuzzy inference can be expressed as follows [15, 16].Fuzzy comprehensive evaluation is carried out from the second level of factors. We can set the evaluation object to be the factor UIJ in the second level, the membership degree of the kth element in the evaluation set to be rijk, and then the single-factor membership degree matrix of the second level is(8)Ri=ri11ri12Lri1pri21ri22Lri2pMMMrin1rin2Lrinp.Then, the first-level fuzzy comprehensive evaluation model can be expressed as follows:(9)R=B1B2MBm=r11r12Lr1pr21r22Lr2pMMMrm1rm2Lrmp.


## 3.3. Elman Neural Network

tElman neural network is widely used for its large-scale parallel distributed structure and learning ability and generalization ability. The main advantages include nonlinear analysis capability, convenient input/output mapping, adaptive capability, evidence response, background information, strong fault tolerance, and VLSI (very large scale integrated) implementation. The key to the nonlinear ability and learning ability of neural network lies in the continuous modification of weights. There are two methods for recurrent network training, one is batch mode and the other is online mode. Like the BP neural network, the Elman neural network uses the ordered chain rule for learning and derivation. The output layer weights can be expressed as follows [17, 18].For the input layer, the Elman network can be represented as follows:(10)xi0=xik.For the hidden layer, the Elman network can be expressed as follows:(11)si1=∑j=1n0wij0xj0k+∑j=1n1wij2cj0k,xi1=f1si1k.For the association layer, the Elman network can be expressed as follows:(12)si2k=xi1k−1,cik=si2k.For the output layer, the Elman network can be represented as follows:(13)si3k=∑j=1n1wij1xj1k,yik=f2si3k,−∂Ek∂wij1=−∂Ek∂yik·∂yik∂wij1=eik·f2′si3k·xj1k.Similarly, the implicit value in the network can be expressed as follows.(14)−∂Ek∂wij0=−∑l=1r∂Ek∂ylk·∂ylk∂wij0=∑l=1relk·f2′sl3k·wij1k·∂xi1k∂wij0.Through comprehensive calculation, we can get(15)−∂Ek∂wij0=∑l=1relk·f2′sl3k·wij1k·χijik,χijik=f1′si1k·xj0k+∑m=1n1wim2·χijmk−1.In the actual operation process, the neural network algorithm can be used to replace the traditional BP neural network for research. At the same time, the correlation and difference between the two can also be reflected in the prediction process. Figure2 is a flow chart of the calculation process of the neural network.Figure 2
The calculation flow chart of the Elman neural network.


## 4. Psychological Adjustment and Emotional Health Care in Music Teaching Based on Big Data Analysis

### 4.1. The Application of Wavelet Packet Analysis in Music Teaching

In the specific piano performance teaching, in addition to requiring the performer to deeply understand the composer’s emotions and inner world when shaping the music image, the performer also needs to recreate the piano works based on their own characteristics. Only in this way can the musical composition match the characteristics of its own performance, which can allow the audience to produce the best musical experience. In this process, performance breathing is also a very important part of the content, and students must have a deep understanding of the performance works in the performance. On this basis, students should fully understand the breathing law of performance and then make secondary creations on this basis.Combining with specific teaching examples, it can be seen that students’ breathing is affected by their own emotions. When they are more panicked when playing, they may produce nervous emotions, which will affect their breathing during performance. This requires students to accumulate certain breathing experience in specific training. In the process, studies have demonstrated that it is possible to perform quantitative studies using the collected signals of breathing vibrations. Figure3 is a player’s breathing vibration signal collected during a certain piano performance. As shown in Figure 3, the player’s breathing vibration signal exhibits the regularity of continuous oscillation with time.Figure 3
Typical breathing vibration signal.As an unsteady vibration signal, the player’s breathing vibration signal shows strong nonlinearity and randomness. It is well known that the signal intrinsic characteristics of such nonstationary signals can be obtained by means of signal analysis [19, 20]. The existing signal analysis methods mainly include Fourier transform, wavelet analysis, wavelet packet analysis, and Hilbert transform. Among them, the Fourier transform is suitable for the processing of steady-state signals, which can transform the signal from the time domain to the frequency domain representation through the corresponding window function. However, this transformation method is not suitable for the processing of nonstationary signals. Wavelet analysis can optimize the Fourier transform principle by changing the size of the window function. It can decompose the original signal into multiple layers of components. However, in the decomposition process of wavelet analysis, the high-frequency part of the signal is deleted and the resolution of high frequency is reduced. In order to overcome the defects of wavelet analysis, wavelet packet analysis [21, 22] also decomposes the high-frequency part of the signal, which effectively improves the high-frequency resolution of the signal.The Daubechies wavelet series has good compactness, smoothness and symmetry, so it is widely used in unsteady signal processing. For the same color signal, db5∼db10 are used for 9-layer decomposition, respectively, and the reconstruction error is shown in Figure4. As shown in Figure 4, the reconstruction error of db10 is the smallest, so this paper uses db10 as the wavelet basis function for subsequent research.Figure 4
Reconstruction errors of different wavelet basis functions.Assuming that ann-level decomposition of the signal with frequency w results in 2n sub-bands, with each sub-band width being w/2n:(16)xt=∑j=02n−1xn,j,xn,j is the reconstructed signal corresponding to the jth frequency band of the nth layer, j = 1,2,3, ……,2n−1.LetEn, j represent the signal energy value corresponding to the frequency band of xn, j gives(17)En,j=∫xj,it2dt=∑k=1mzi,k2,where zi,k is the amplitude corresponding to the discrete points of the sub-band, k is the number of discrete points, and m is the length of the collected data.The total vibration energy of the signal can be expressed as [23, 24](18)E=∑j=12jEn,j.The energy percentage of each frequency (T n, j) band can be expressed as follows:(19)Tn,j=En,jE.The signal is decomposed into 9 layers using the “db10” basis function. According to formulas (1–4), the wavelet packet energy calculation is carried out through the MATLAB platform. The energy percentages of the sub-bands are shown in Figure 5.Figure 5
Spectrogram of the player’s breathing signal.


### 4.2. The Specific Application of Big Data Analysis Technology

Secondly, it is also very important to adjust the breathing from the physiological and psychological aspects. First, breathing is a physiological characteristic. In specific piano performances, it is not enough for students to maintain normal breathing. For example, some students may feel physical tension or muscle fatigue when playing, and the brain and thinking cannot be connected, which makes the performance unsatisfactory. To a certain extent, this is related to the unnatural breathing adjustment of students, and teachers need to take the initiative to help students relieve tension. This teaching mode can enhance students’ confidence and return to normal breathing. In this way, the normal breath and the breath during performance can be effectively integrated to ensure that a good breathing state is always maintained during the specific performance. In addition, teachers should guide students to coordinate breathing and performance when carrying out piano teaching activities, so that students can adjust their breathing independently during performance. Many students often have the problem of arm soreness in specific performances. Teachers should help students to correct their movements in time, so that students can master the correct breathing method. Through this operation mode, it can accurately adjust the breathing rhythm and bring out the normal performance level. At the same time, the psychological impact cannot be ignored. Psychological factors have a direct impact on students’ piano performance. Students can perform normally in daily practice. However, when it comes to the exam, it will be affected by nervousness, and various emergencies will appear one after another, such as shortness of breath, playing wrong sounds, etc., which seriously affects the performance of the normal level. Therefore, teachers should encourage students to adjust their mentality, enhance their confidence in playing, and accurately identify playing and notation. This adjustment mode can eliminate the interference of bad psychology in time, stabilize one’s own emotions, and lay the foundation for smooth piano performance.Based on the characteristics of breathing changes in the process of piano performance in universities as the research object, this paper uses the three big data processing technologies introduced above to predict and study the physiological and psychological factors involved in the process of piano performance, in order to complete the purpose of quantitative prediction analysis.It is well known that the closer the square of the correlation coefficient (r2) is to 1, the smaller the root mean square error(RMSE), the median absolute error (MAPE), and the mean absolute percentage error (MEDAE), indicating higher prediction accuracy [25]. Among them, RMSE can be obtained by the following formula:(20)RMSE=1n×∑i=1nyi−xi2.Figures6 and 7 show the prediction performance of the three big data technologies introduced in this paper.Figure 6
The square of the correlation coefficient corresponding to the three big data technologies.Figure 7
Quantitative predictor values of three big data technologies.It should be specially pointed out that I represents the multidimensional support vector machine optimized, II represents fuzzy neural inference system, and III represents the Elman neural network.As shown in Figures6 and 7, the square of the correlation coefficient corresponding to fuzzy neural inference system is the largest, and the maximum value is 0.9851. In addition, from the perspective of prediction indicators, the root mean square difference corresponding to fuzzy neural inference system is 0.3687. The three predictors are all the smallest. Among them, the minimum value of the root mean square difference is 0.1549. This comparison result shows that the prediction effect corresponding to the fuzzy neural inference system is the best.It can be found that the fuzzy neural network reasoning system can be used as a representative big data technology in the innovative research of psychological adjustment and emotional health care in music teaching in universities.In addition, in order to systematically evaluate the prediction effect of the fuzzy neural inference system, the 3D cloud map of the prediction data corresponding to the two aspects obtained by the inference system based on the fuzzy neural network is drawn in Figure8. As shown in Figure 8, the predicted data of the physiology and psychology during the piano performance obtained by the fuzzy neural inference system show good continuity and consistency.Figure 8
3D prediction effect of the fuzzy neural network.


## 4.1. The Application of Wavelet Packet Analysis in Music Teaching

In the specific piano performance teaching, in addition to requiring the performer to deeply understand the composer’s emotions and inner world when shaping the music image, the performer also needs to recreate the piano works based on their own characteristics. Only in this way can the musical composition match the characteristics of its own performance, which can allow the audience to produce the best musical experience. In this process, performance breathing is also a very important part of the content, and students must have a deep understanding of the performance works in the performance. On this basis, students should fully understand the breathing law of performance and then make secondary creations on this basis.Combining with specific teaching examples, it can be seen that students’ breathing is affected by their own emotions. When they are more panicked when playing, they may produce nervous emotions, which will affect their breathing during performance. This requires students to accumulate certain breathing experience in specific training. In the process, studies have demonstrated that it is possible to perform quantitative studies using the collected signals of breathing vibrations. Figure3 is a player’s breathing vibration signal collected during a certain piano performance. As shown in Figure 3, the player’s breathing vibration signal exhibits the regularity of continuous oscillation with time.Figure 3
Typical breathing vibration signal.As an unsteady vibration signal, the player’s breathing vibration signal shows strong nonlinearity and randomness. It is well known that the signal intrinsic characteristics of such nonstationary signals can be obtained by means of signal analysis [19, 20]. The existing signal analysis methods mainly include Fourier transform, wavelet analysis, wavelet packet analysis, and Hilbert transform. Among them, the Fourier transform is suitable for the processing of steady-state signals, which can transform the signal from the time domain to the frequency domain representation through the corresponding window function. However, this transformation method is not suitable for the processing of nonstationary signals. Wavelet analysis can optimize the Fourier transform principle by changing the size of the window function. It can decompose the original signal into multiple layers of components. However, in the decomposition process of wavelet analysis, the high-frequency part of the signal is deleted and the resolution of high frequency is reduced. In order to overcome the defects of wavelet analysis, wavelet packet analysis [21, 22] also decomposes the high-frequency part of the signal, which effectively improves the high-frequency resolution of the signal.The Daubechies wavelet series has good compactness, smoothness and symmetry, so it is widely used in unsteady signal processing. For the same color signal, db5∼db10 are used for 9-layer decomposition, respectively, and the reconstruction error is shown in Figure4. As shown in Figure 4, the reconstruction error of db10 is the smallest, so this paper uses db10 as the wavelet basis function for subsequent research.Figure 4
Reconstruction errors of different wavelet basis functions.Assuming that ann-level decomposition of the signal with frequency w results in 2n sub-bands, with each sub-band width being w/2n:(16)xt=∑j=02n−1xn,j,xn,j is the reconstructed signal corresponding to the jth frequency band of the nth layer, j = 1,2,3, ……,2n−1.LetEn, j represent the signal energy value corresponding to the frequency band of xn, j gives(17)En,j=∫xj,it2dt=∑k=1mzi,k2,where zi,k is the amplitude corresponding to the discrete points of the sub-band, k is the number of discrete points, and m is the length of the collected data.The total vibration energy of the signal can be expressed as [23, 24](18)E=∑j=12jEn,j.The energy percentage of each frequency (T n, j) band can be expressed as follows:(19)Tn,j=En,jE.The signal is decomposed into 9 layers using the “db10” basis function. According to formulas (1–4), the wavelet packet energy calculation is carried out through the MATLAB platform. The energy percentages of the sub-bands are shown in Figure 5.Figure 5
Spectrogram of the player’s breathing signal.


## 4.2. The Specific Application of Big Data Analysis Technology

Secondly, it is also very important to adjust the breathing from the physiological and psychological aspects. First, breathing is a physiological characteristic. In specific piano performances, it is not enough for students to maintain normal breathing. For example, some students may feel physical tension or muscle fatigue when playing, and the brain and thinking cannot be connected, which makes the performance unsatisfactory. To a certain extent, this is related to the unnatural breathing adjustment of students, and teachers need to take the initiative to help students relieve tension. This teaching mode can enhance students’ confidence and return to normal breathing. In this way, the normal breath and the breath during performance can be effectively integrated to ensure that a good breathing state is always maintained during the specific performance. In addition, teachers should guide students to coordinate breathing and performance when carrying out piano teaching activities, so that students can adjust their breathing independently during performance. Many students often have the problem of arm soreness in specific performances. Teachers should help students to correct their movements in time, so that students can master the correct breathing method. Through this operation mode, it can accurately adjust the breathing rhythm and bring out the normal performance level. At the same time, the psychological impact cannot be ignored. Psychological factors have a direct impact on students’ piano performance. Students can perform normally in daily practice. However, when it comes to the exam, it will be affected by nervousness, and various emergencies will appear one after another, such as shortness of breath, playing wrong sounds, etc., which seriously affects the performance of the normal level. Therefore, teachers should encourage students to adjust their mentality, enhance their confidence in playing, and accurately identify playing and notation. This adjustment mode can eliminate the interference of bad psychology in time, stabilize one’s own emotions, and lay the foundation for smooth piano performance.Based on the characteristics of breathing changes in the process of piano performance in universities as the research object, this paper uses the three big data processing technologies introduced above to predict and study the physiological and psychological factors involved in the process of piano performance, in order to complete the purpose of quantitative prediction analysis.It is well known that the closer the square of the correlation coefficient (r2) is to 1, the smaller the root mean square error(RMSE), the median absolute error (MAPE), and the mean absolute percentage error (MEDAE), indicating higher prediction accuracy [25]. Among them, RMSE can be obtained by the following formula:(20)RMSE=1n×∑i=1nyi−xi2.Figures6 and 7 show the prediction performance of the three big data technologies introduced in this paper.Figure 6
The square of the correlation coefficient corresponding to the three big data technologies.Figure 7
Quantitative predictor values of three big data technologies.It should be specially pointed out that I represents the multidimensional support vector machine optimized, II represents fuzzy neural inference system, and III represents the Elman neural network.As shown in Figures6 and 7, the square of the correlation coefficient corresponding to fuzzy neural inference system is the largest, and the maximum value is 0.9851. In addition, from the perspective of prediction indicators, the root mean square difference corresponding to fuzzy neural inference system is 0.3687. The three predictors are all the smallest. Among them, the minimum value of the root mean square difference is 0.1549. This comparison result shows that the prediction effect corresponding to the fuzzy neural inference system is the best.It can be found that the fuzzy neural network reasoning system can be used as a representative big data technology in the innovative research of psychological adjustment and emotional health care in music teaching in universities.In addition, in order to systematically evaluate the prediction effect of the fuzzy neural inference system, the 3D cloud map of the prediction data corresponding to the two aspects obtained by the inference system based on the fuzzy neural network is drawn in Figure8. As shown in Figure 8, the predicted data of the physiology and psychology during the piano performance obtained by the fuzzy neural inference system show good continuity and consistency.Figure 8
3D prediction effect of the fuzzy neural network.


## 5. Conclusion

College music teachers and musicians are groups with their own special psychological state of the industry. In the teaching process, how to adjust to the psychological particularity of music teaching and the psychological status of music teachers in universities in order to improve the quality of teaching is an urgent problem to be paid attention to. With the advent of the era of big data, new innovative applications such as big data technology have been applied to the reform and innovation process of music teaching in Chinese universities. In order to quantify the evaluation indicators of psychological adjustment and emotional health care in music teaching, this paper takes piano teaching in universities as an example and uses big data analysis technology to analyze the physiological and psychological evaluation indicators involved in the piano teaching process. To this end, this paper introduces three different big data technologies and uses them to predict the physiological and psychological data involved in the piano teaching process. The prediction effect shows that the prediction effect obtained by the fuzzy neural network system is the best. Its RMS deviation is only 0.16. The square of the correlation coefficient is the largest, and the maximum value reaches 0.98. In addition, the three-dimensional data cloud map involved in the prediction data show that the prediction data obtained by the fuzzy neural network system exhibits good continuity.



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*Source: 4501189-2022-09-16.xml*