Review and Comparison of Instrumental Music Methods for Content

Front Psychol. 2021; 12: 657788.

Educational Psychology-Based Strategy for Instrumental Music Instruction in Normal Higher

Received 2021 January 24; Accepted 2021 Apr xx.

Data Availability Statement: The raw data supporting the conclusions of this article will be fabricated available past the authors, without undue reservation.

Abstract

The study is intended to explore the teaching fashion of instrumental music teaching in normal college, then equally to have qualified instrumental music pedagogy talents. Based on relevant content of educational psychology, the current instrumental music teaching model of normal college should be reformed in terms of curriculum content, pedagogy mode, and teaching methods, and a more systematic and standardized instruction model should be established. As per Orff's music teaching method, a music recommendation model is established based on the convolutional neural network model to provide students with a positive and happy learning surround for instrumental music, and music materials that meet their personal preferences and performance level through user information. The outcomes show that the designed music recommendation model has a music recommendation accurateness rate of 0.3 and a retrieve rate of 0.29 when the recommendation list is 30, which conforms to the general rules of the music recommendation system. The study is expected to provide reference for establishment of a standardized and systematic instrumental music teaching strategy in normal college.

Keywords: normal college, instrumental music instruction, musical didactics strategy, teaching mode, music teaching methods

Introduction

The goal of musical education in normal higher is to train students to exist music talents who have proficient music literacy and suit to the didactics tasks of master and secondary schools, and the training process is highly professional person. Therefore, in training process in normal higher, only imparting professional noesis and related teaching skills can't meet the teaching needs. Students should consolidate and apply the knowledge they have learned and then as to gain valuable teaching experience. Instrumental playing is the foundation of instrumental music instruction, and this process can cultivate students' cultural aesthetics (Anglada-Tort and Sanfilippo, 2019). Traditional instrumental music teaching is no longer suitable for electric current needs due to old teaching content, unmarried teaching methods, without integration with related fields, and excessive pursuit of performance skills. Therefore, it is imperative to reform instrumental music educational activity. Students in normal higher need to take educational practice in primary and secondary schools, adapt to the teaching environment of the schoolhouse, and experience the change of the office, which provides a basis for personal professional development (Burak, 2019).

Rickels et al. (2019) studied the motivation and influence of involvement and disinterest in the music teaching profession. They used the principal component discriminant analysis method to compare iii occupational categories of music teaching, other music, and other not-music. Experimental results showed that, the right classification rate of this method was 69.viii%. I gene tin can distinguish 2 music groups from the non-music group, and the other three factors can distinguish the partial music group from non-music group. Therefore, the choice of profession seems to exist diversifying, which cannot exist predicted through a single factor (Rickels et al., 2019). Schiavio et al. (2020) studied 11 music expert teachers based on their individual and collective surround didactics practice. They adopted a basic theory-based method to identify two interrelated themes in the original information: teaching problems and professional person development. In these two themes, the concept of "presence" was a decisive feature for the comparing. In a collective surround, teachers have a depression sense of presence, while higher expectations of people and college requirements (cognition, teaching, etc.) of learners volition cause teachers to participate more in the pedagogy. Experimental results showed that, in a commonage surround, teachers tin also be learners, reducing their own sense of presence. Students' sense of responsibility and learning ability were fostered by establishing a hybrid instruction expansion system (Schiavio et al., 2020). Abramo and Campbell (2019) surveyed 12 senior students majoring in music education and preschool teaching, about their expectations and beliefs almost the relationship between them and their co-teachers before their teaching experience. The results of the questionnaire survey showed that, the participants hope that the co-teachers can be their friends. The senior students thought that, their relationship with the co-teachers was a standardized practice; but they also hoped that, the co-teachers can provide emotional back up and share personal stories. Therefore, based on these findings, teachers may give co-teachers straight educational guidance, with philosophical guidance as the center, and envisage teaching experience across the one-to-ane guidance relationship (Abramo and Campbell, 2019). Vasil et al. (2019) studied the pop music instruction in music pedagogy, based on the theory of educational psychology. The experiment found that, the popular music teaching method can enable students to have the power to call up critically. Teachers can create by interacting with students, to teach music cognition (Vasil et al., 2019). Menz et al. (2020) studied pre-service teachers' misunderstanding of educational psychology, which may pose a threat to actual pedagogy goals. Through a questionnaire survey on the misunderstandings about educational psychology amid pre-service teachers, information technology was found that, only a few teachers can change their attitude toward misunderstandings. Therefore, misunderstandings of educational psychology are common among pre-service teachers (Menz et al., 2020).

To the best of our knowledge, the inquiry on instrumental music educational activity in normal college mainly focuses on the interaction between teachers and students. Regarding low didactics efficiency and low quality of teaching in traditional instrumental music teaching in normal college, it is necessary to cultivate qualified talents for instrumental teaching in normal colleges, to raise students' enthusiasm for learning instrumental music. In the written report, the instrumental music education strategies in normal higher are analyzed based on educational psychology, and reform is recommended from v aspects of the learning theory, education elements, curriculum elements, educational activity procedure, and teaching mode, so as to have a more systematic instrumental music instruction method. Farther, combined with Orff's music didactics method, students are provided with a positive and encouraging learning environment, and suitable musical materials are recommended based on the CNN, and so that students can exercise in an environment that matches their music preferences. In decision, the Orff's music teaching method is combined with CNN to provide students with a pleasant instrumental music learning environment, aiming to promote the overall development of instrumental music education, establish a systematic and standardized instrumental music teaching method, and comprehensively improve teaching quality. Also, information technology is expected to accept chief and secondary school music teachers with skillful functioning skills, mental health, and a solid theoretical foundation.

Materials and Methods

Educational Psychology-Based Instrumental Music Educational activity Reform

Instrumental music teaching is a very of import music pedagogy mode, which tin stimulate students' enthusiasm for learning instrument, and cultivate students' creative thinking and perfect personality. Instrumental music teaching reform in normal college is affected by the traditional music teaching model, and lacks relevant theoretical back up and guidance. Therefore, the reform procedure is restricted. From the perspective of educational psychology, the reform of instrumental music didactics mode should have account into students' cognitive styles, and the connectedness betwixt teaching mode and teaching strategy should be considered. Cognitive style is the habitual attitude that individuals have when processing and organizing information in cognitive activities such every bit feeling and understanding (Burwell et al., 2019).

The relevant background knowledge of instrumental music should be added to instrumental music teaching, such as the origin of musical instruments, evolution history, art form, cultural background, composer and creative manner, representative composition assay, and repertoire appreciation can assist students understand the cultural deposits of instrumental music and better the overall artistic quality of students. Equally a result, their professional understanding is more comprehensive and their functioning ability is improved (Taft et al., 2020). Besides the traditional instrumental music works, students should also learn globe folk music works and mod works. By playing unlike styles of works, they can sympathize the characteristics of unlike styles of writers and genres, and enhance their aesthetic ability (Öztuğ and Saldun, 2020). According to teaching characteristics in normal higher, content related to music teaching in primary and secondary schools is introduced for learning.

Traditional instrumental music teaching more often than not adopts one-to-one teaching method, which has depression efficiency. What'south more, the educational activity quality can't be guaranteed, because students from different majors have different learning abilities (Julia et al., 2020). There are two types of courses for students: elective courses and compulsive courses. With the deepening of the pedagogy, the focus has changed. From the single instrumental music performance at the start, students begin to have practical grooming courses and rehearsal courses (Kim, 2019). Hence, the corresponding instruction mode and instruction methods need to be adjusted to better meet the teaching requirements. For dissimilar curriculum models, different teaching methods should be adopted. For students majoring in instrumental music, a one-to-i teaching method should be adopted to conduct targeted observations on students who already accept certain operation abilities and skills, and solve different problems in the performance of students (Bayley and Waldron, 2020). At the aforementioned fourth dimension, teachers should formulate a personalized learning plan and choose a piece of music suitable for students' level, to enhance students' performance skills and inspire students to understand music more deeply.

For students who take instrumental music as elective courses, one-to-many teaching method should exist adopted. Because they more often than not major in vocal music or piano and other subjects, they haven't learned instrumental music earlier. The elective courses can aid students principal instrumental performance and band rehearsal, thus condign a more comprehensive talent (Matthews and López, 2020). Therefore, the focus of teaching is on the basic knowledge of instrumental music and basic performance skills. By agreement the history of musical instruments and mastering the basic playing methods of musical instruments, students therefore amend their personal operation ability and grouping ensemble ability (Cho, 2019). In instrumental music teaching, great importance should exist attached to the training of students' practical skills. Students should utilize the knowledge of instrumental music they have learned to practise, and observe problems in learning through practise. Information technology is also feasible to organize open classes and concerts, so that students can have training, thereby enhancing their actual performance ability. Therefore, in the instrumental music education course, it is necessary to combine specific educational activity modes, institute a more than scientific and complete instrumental music instruction organisation on the basis of the original instruction, and integrate actual skill grooming to improve the pedagogy level and quality (Palkki and Sauerland, 2019).

Information Engineering science in Instrumental Music Teaching in Normal College

Accompanied by social informatization, the introduction of information engineering in teaching has greatly challenged the traditional education. Therefore, it is imperative to promote informatization of instrumental music teaching in normal colleges, so as to establish modern educational technology with computer applied science as the core. With the advocacy of technology, teachers need to combine computer applied science to collect, organize, and display various instrumental music teaching materials, and use them in courses. The application of computer technology can stimulate teachers' creativity and enthusiasm for teaching, and tin can too raise students' enthusiasm for learning. The use of calculator applied science to synthesize piano accessory and harmony accompaniment. And so, students can ensemble with accompaniment, which cultivates students' teamwork ability and sensation. Students should be encouraged to participate in various performance activities to accumulate performance experience and discover their ain shortcomings, so as to better their ability. Students tin also larn instrumental music by listening to related lectures or concerts. Therefore, multiple teaching methods tin enrich the instruction content of instrumental music teaching in normal college, amend students' artistic appreciation ability, expand students' innovative thinking, and elevate students' instrumental operation skills (Zhang et al., 2020).

Since normal college aims to train students to be teachers, students should not only focus on musical instrument skills, only as well learn teaching methods, and combine instrumental music teaching, educational psychology, and instrumental music ensemble with instrumental music courses. Instrumental music educational activity elaborates on the basic content, procedure, rules, and methods of musical musical instrument education (Cruel et al., 2020), which tin aid students have a comprehensive and in-depth understanding of the relevant elements of learning, guide them to grasp the rules, and improve their playing level. The instrumental music teaching method tin as well provide theoretical guidance for students' hereafter education work. In the courses of instrumental performance and instrumental music educational activity, students learn the teaching methods while learning operation skills. In this learning process, students learn certain instrumental performance skills and deepen their agreement of musical instruments. At the aforementioned time, they acquire how to teach by observing the didactics methods of teachers (Freer and Evans, 2019).

Educational psychology theory is to analyze the human relationship between behavior procedure, psychological process, and teaching process. Relevant psychological theories are instrumental in explaining students' music behavior and music experience, improving students' performance skills by enhancing their psychological quality. Instrumental music courses are an integral role of music pedagogy in normal college, which is expected to strengthen students' agreement of collective instrumental performance by perfecting students' performance and teamwork skills. How to make students have a pleasant learning attitude in teaching is the focus of educational psychology. The Orff's music teaching method is to establish a positive and encouraging music learning environment for students, and then that students can have a systematic instrumental music learning method. Its secondary aim is to have qualified primary and secondary school music teachers.

Orff's Music Didactics Method Combined With CNN in Instrumental Music Educational activity in Normal College

Traditional instrumental music teaching emphasizes the pedagogy of knowledge and skills, and the static teaching method is adopted. However, due to the characteristics of music itself, students are required to communicate with the audience through music. Therefore, the Orff method is applied to optimize the teaching strategies of instrumental music in normal higher. Pedagogy strategy includes five parts: learning theory, teaching elements, curriculum elements, education process, and teaching way. Pedagogy elements include didactics objectives, content, methods, and resource (Ros-Morente et al., 2019). Curriculum elements include teachers, students, pedagogy materials, and environment. When designing teaching strategies, information technology is necessary to consider different teaching elements, analyze the role of teachers in teaching, and think about the instruction methods and resources needed to better serve students. Orff's music education method was created past German music educator Carl Orff. Orff believes that successful music teaching is positive and encouraging, allowing students to experience the value of their own development (Cowie, 2020). Its purpose is to show the diversity and richness of music, and to encourage students to learn in activities. As a result, students obtain things across music learning, such as imagination, teamwork ability, self-learning ability, and inventiveness (Kellems et al., 2019). Therefore, the use of Orff'southward music pedagogy method in the instrumental music pedagogy in normal college makes teachers focus on the quality of pedagogy and the cultivation of students' playing skills.

As per Orff's music didactics method, students are provided with a positive and encouraging music learning environment to raise their learning interests and reduce teachers' pressure in class. Specifically, a music recommendation algorithm is established based on CNN to predict the hidden features of the music materials, and the low-dimensional vector of the music features is then obtained. Finally, the practice materials are recommended according to the user's preference features. The music recommendation procedure is shown in Figure 1.

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CNN-based music recommendation model.

The music recommendation model includes an audio feature extraction module, a recommendation algorithm module, and a user modeling module. The sound feature extraction module can perform preprocessing and feature extraction on music. The recommendation algorithm can predict the potential characteristics of music according to the regression model and combine user preferences to obtain the matching degree between the user and the music, and generate a list of music that the user may like. The user modeling module can collect students' choices and build user preference feature models. The whole model is divided into two parts: regression training and prediction recommendation (Bertka et al., 2019).

In music information retrieval, it is necessary to excerpt the characteristics of the audio to reflect the essential data of the music to distinguish different music (Vasil, 2019). Audio features can exist divided into two types, namely, time domain features and frequency domain features. In the study, brusk-fourth dimension Fourier transform is used to excerpt audio features. For non-stationary non-periodic sound signals whose frequency spectrum changes continuously over time, they tin can be changed from the time-domain signals to frequency-domain signals. The frequency component of the point can be seen intuitively, but without the time-domain data, the relationship between the frequency distribution and fourth dimension can't be obtained (Schedl, 2019). The Short Time Fourier Transform (STFT) is a two-dimensional representation method of time domain and frequency domain. Sound is a signal that changes slowly with fourth dimension. The STFT can dissever a long-term signal into frames, and perform Fourier transform on each frame to limited the characteristics of the audio signal at a certain time (Salvador, 2019). The equation for performing short-time Fourier transform on the source signal x(t) is as follows.

$S T F T {x (t)} (τ, θ) = \int_{- \infty}^{\infty} ten (t) due west (t - τ) e^{- j ω t} d t$

(1)

west(t) is a window function; Due south T F T{} represents the STFT; eastward ^−jωt represents the negative frequency domain; t, τ, and θ are parameters in STFT. The discrete betoken is expressed as follows.

$S T F T {ten (n)} (m, ω) = \sum_{n = - \infty}^{\infty} x (n) w (n - grand) e^{- j ω due north}$

(2)

w(n−yard) represents the window office sequence, and n represents the discrete time bespeak. After the transformation of the audio signals, the amplitude and phase data of unlike frequencies are obtained (Gunawan and Suhartono, 2019). The phase data can be converted into a spectrogram of amplitude information. The equation of transformation betwixt short-time power spectrum and STFT is as follows.

The spectrogram can be regarded as a two-dimensional signal image which is formed by superimposition of each frame of the STFT. And so, a spectrogram containing two-dimensional signals of the time domain and frequency domain is obtained.

The Music Recommendation Model Based in Convolutional Neural Network

The neural networks are used to perform feature analysis on spectrograms of unlike types of music, together with spectrum images, and the music in the data set is classified according to music genre tags. CNN is a feedforward neural network that includes convolution operations and deep structures. Its bones structure includes the input layer, the convolution layer, the pooling layer, the fully connected layer, and the output layer. As shown in Figure 2, using local connections avoids the trouble of information loss under a large number of parameters (Du et al., 2019). First, the input layer reads the information matrix of the input data; then it outputs to a convolutional layer with multiple feature surfaces. Each feature surface has multiple neurons, and the input of each neuron node is the upshot of the previous network block. The depth information of the data can exist extracted using the convolution block in the convolution layer to obtain college-dimensional characteristic data. The pooling layer reduces the dimensionality of the high-level features output by the convolutional layer. The neurons between the layers in the fully connected layer are fully continued, and the feature data obtained past the convolutional layer and the pooling layer is classified. The output layer uses the Softmax part to classify features and output the results. The probability distribution of each category is calculated, the category with the highest probability is the category of the exam sample (Zhang and Yang, 2019).

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Compared with artificial neural networks, CNN introduces the concepts of local perception, weight sharing, and downsampling, which greatly improves the performance of the network. Local perception ways that each neuron in the CNN only connects to some neurons in the next layer, thereby reducing the weight parameters. Therefore, the connexion between local information is close, and the correlation with farther information is weak. Only the neighboring information needs to be perceived, which can greatly reduce the computing time (Alkalay and Dolev, 2019; Wen, 2020). As the number of layers increases, local feature extraction tin exist continued on the characteristic data extracted by the previous layer to obtain global information of the input information. In CNN, weight parameters or convolution kernels are shared betwixt each group, instead of each connection having its own weight. The convolution kernel has a specific feature in a certain area, and then this convolution kernel can too exist used in other areas with the same feature. CNN uses downsampling technology to compress the input information, reduce the full volume of input information, and reduce the over-fitting phenomenon acquired past likewise many weight parameters. In the meanwhile, because the data space size is compressed, the amount of adding of CNN is greatly reduced and the calculation speed is accelerated. Table i shows CNN parameter setting.

Table i

CNN parameter setting.

Number of network layers	2-layer	Learning charge per unit	0.1
Convolution kernel size	v × 5	Loss function	Quadratic mean foursquare function
Optimization algorithm	Adagard algorithm	Mini-batch	30
Activation office	Relu role	Initial weight	ane.5

The Database Used and the Experimental Environs

A big amount of data is needed to railroad train and test the designed music recommendation model, but due to copyright restrictions, most databases do not straight provide audio files. Therefore, to train and test the designed model, the audio files used in the instrumental music educational activity in normal college are collected to establish a data set containing sound files and user beliefs records, as well as unlike users' vocal playback records. The data is preprocessed to obtain a data set that meets the experimental requirements. The data set contains 700 common practise songs downloaded from the website. Among them, 400 songs are used as the training set, and the remaining 300 songs are used every bit the exam set. Xv students majoring in instrumental music teaching of a normal college are selected as the research subjects, with each student's song playing in 60 days recorded to obtain a data gear up of users and music playing times (Solanki and Pandey, 2019; Deng et al., 2021). The accuracy charge per unit and call back rate are used as evaluation indexes. Tabular array two is the experimental environs setting.

Tabular array 2

The experimental surroundings.

Organisation	Win10	RAM + internal storage	16 GB + 512 GB
CPU	i7-7700	Software	Matlab R2015a_x64
GPU	GeForce GTX 960	Conquering equipment	Zoom H6+Rode NTG3

Results and Discussion

The mean square error (MSE) is used to plant the loss function. Then, the training set congenital is used to train the CNN model, and Figure three are the experimental results.

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The loss curve of the trained model.

It is axiomatic that as the number of iterations increases, the loss error of CNN begins to subtract quickly. When the number of iterations reaches 10, the mistake drops to 0.128 and then tends to converge. According to the loss curve, the grooming of the model meets the expected effect, which tin can well exam the predictive power of the arrangement.

To verify the applicability of the music recommendation algorithm, the recommendation accuracy nether different recommendation list lengths is tested. The length of the recommended list is set up to 10, 15, 20, 25, 30, and the experimental results are shown in Figure 4.

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The recommendation results under unlike list length.

It is evident from Figure 4 that as the length of the recommendation list increases, the accuracy charge per unit is continuously decreasing, and the retrieve rate is continuously increasing. When the recommended list length is 10, the accurateness charge per unit is well-nigh 0.41, and the call back rate is nigh 0.128. When the recommended listing length increases to 30, the two are 0.3 and 0.29, respectively.

To verify the effectiveness of the recommendation algorithm, the existing data set is used to conduct comparative experiments on different recommendation algorithms. The experimental results are shown in Figure five.

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Recommendation accuracy of unlike algorithms.

Information technology is evident from Figure five that under the same length of the recommendation list, the accurateness of the recommendation algorithm designed in this report is the highest. The reason may be that the algorithm designed in this study studies the interaction betwixt users and music, so that CNN can improve learn user habits.

By incorporating the educational psychology into the instrumental music teaching of normal college, the instrumental music teaching methods, music psychology, and instrumental ensemble will be integrated to aid students better principal the knowledge of instrumental music and proceeds practical feel, providing a foundation for time to come educational activity work (Chen, 2020; Deng et al., 2021). In improver, the introduction of CNN provides students with an active instrumental learning environment. Orff'southward music teaching method is referred to make students interested in learning during the learning process. The accuracy rate of the Western music recommendation model based on the CNN algorithm is as high equally 96.5% (Chen, 2019), but the model designed in this study has a wider telescopic of application and a higher overall recommendation accuracy charge per unit, creating a new pedagogy mode of instrumental music in normal college. The pre-service education is the beginning step of the professional evolution for normal college students. Through educational practice, students tin can experience didactics piece of work in the front end line, accumulate educational activity experience, and cultivate professional person awareness and responsibleness. Secondly, through the educational exercise, they experience role-changing from students to teachers, so that they tin can face dissimilar teaching situations (Numanee et al., 2020). Finally, they tin independently engage in first-line instrumental music educational activity jobs by using their theoretical cognition and educational methods to perceive and reverberate on the teaching situations they encounter.

Conclusion

To explore the teaching strategies of instrumental music instruction in normal college and enhance students' enthusiasm for learning instrumental music, the educational activity method reform of instrumental music education in normal higher is recommended from many aspects based on educational psychology, and a more systematic and standardized method of instrumental music pedagogy is established. Furthermore, the Orff music didactics method is combined with the CNN to establish a music recommendation model to provide students with do materials that match their personal preferences and functioning levels, thereby enhancing students' enthusiasm for instrumental music learning. The experimental results bear witness that the designed music recommendation model tin can provide students with practice materials in line with the students' interest. All the same, some shortcomings should be noted in this study. The data set used for training is small, and the number of users used is not enough to train the model well. Therefore, an expanded sample size id needed in subsequent research to improve the predictive performance of the system.

Data Availability Statement

The raw data supporting the conclusions of this article will be made bachelor past the authors, without undue reservation.

Ideals Statement

The studies involving human being participants were reviewed and approved by the Shandong Normal University Ethics Committee. The patients/participants provided their written informed consent to participate in this study. Written informed consent was obtained from the individual(due south) for the publication of whatsoever potentially identifiable images or data included in this article.

Writer Contributions

The writer confirms being the sole contributor of this work and has approved information technology for publication.

Disharmonize of Interest

The writer declares that the enquiry was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher'due south Notation

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Whatsoever product that may be evaluated in this article, or claim that may be fabricated by its manufacturer, is non guaranteed or endorsed by the publisher.

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Source: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8576596/