Music System Research Articles

Application of Internet Information Technology in College Music Education Teaching System

The development of society has higher and higher requirements on the quality of talents, and the demand for talents with comprehensive literacy continues to rise. Using music education as a means to continuously improve the music aesthetic quality of the new generation of young people and establish music aesthetic concepts is an important entry point to improve the quality of the whole people. But the current colleges and universities basically still focus on the traditional education and teaching methods, largely ignoring the importance of music education. In order to improve the comprehensive quality of college students, this paper introduces Internet information technology into the study of music system. The experimental results show that the research on the application of Internet information technology in the music system of colleges and universities can save time and management costs to a great extent and increase the efficiency of educational resources by 3.58%. At the same time, it can implement dynamic music evaluation, reasonably arrange students’ daily music training plans, improve students’ comprehensive quality and music literacy, and integrate music education into students’ lives, so as to increase the effect of music education to a great extent. Besides after the Internet information technology is applied to the music teaching system, the system can be linked with the network platform and resources, which greatly enriched teaching materials for music and information technology.

A Music Teaching Resource Management Model Based on Fuzzy Clustering Algorithm

This study develops and implements a music instructional resource platform based on fuzzy clustering algorithm knowledge in order to make the management of music teaching quality more scientific and standardised. This study presents the construction scheme of a music instructional resource management system in a higher education institution through requirements analysis, function design, system development and implementation, and system testing of the software system. In this study, a system is developed that uses computational intelligence to calculate the content and topics contained in resources based on semantic similarity, generate category labels, and cluster educational resources in order to integrate educational resources and implement effective management. It not only meets the needs of users who want to access the platform system via mobile terminals but also increases the platform system’s utilisation rate. Experiments show that this system’s stability can reach 95.37%. The retrieval accuracy is 96.24%, which is 8.94% better than the traditional method. It has the ability to effectively provide high-quality music teaching resources. Using the instructional resource system proposed in this study will improve teaching effectiveness, reduce learners’ learning burden, and increase learning efficiency.

Designing a National Music System for a Smart Concert Hall Using Neural Network and Wireless Internet of Things

With the fast advancement of science and technology, numerous music systems have emerged. However, there are few folk music in the music industry, for example, pop music, which leads to the inability of folk music lovers to find their favorite music. For this problem, this paper develops a national system of smart concert hall based on a neural network and wireless Internet of things (IoT). It establishes the architecture model of the wireless IoT system of the concert hall by describing the neural network, neuron model, and BP neural network model in detail. Besides, this paper develops the smart music national system based on this model, completes the functional module design of the national music system, and uses the music attribute recommendation model. Finally, it analyzes the scoring performance of the national music system. The results show that when the number of recommended folk music songs is less than 20, the lowest MAE is 0.73.

Evaluating the Vocal Music Teaching Using Backpropagation Neural Network

The vocal music teaching for evaluating performers is affected by multiple factors. Evaluators are greatly influenced by subjective factors in scoring outputs. The backpropagation (BP) neural network provides a novel technology that can theoretically simulate any nonlinear continuous function within a certain accuracy range. The backpropagation neural network is composed of adaptive feedforward learning network that is widely used in artificial intelligence (AI). In addition, the backpropagation neural network can simulate the nonlinear mapping composed of various factors. The novelty of the neural network is that it can model the nonlinear process without knowing the cause of the data, which can overcome the human subjective arbitrariness and make the evaluation outcomes. Furthermore, accurate and effective scoring systems can be designed using neural networks. In this paper, we establish a vocal music evaluation research system in order to objectivize each vocal music teaching evaluation index. To do so, we use the score vector as the input and obtain a reasonable and objective output score through the backpropagation neural network. Moreover, according to the characteristics of the backpropagation neural network, the factors of vocal music teaching evaluation are analyzed, and a backpropagation neural network model for vocal music teaching evaluation and evaluation is constructed. The experimental outcomes demonstrate that the trained backpropagation network can simulate a stable vocal music teaching evaluation research system. Furthermore, we observed that the backpropagation neural network can be well utilized for vocal music teaching evaluation research.

Improved Music Recommendation Algorithm for Deep Neural Network Based on Attention Mechanism

With the increasingly close combination of the Internet and people’s production and life, the total amount of global data and information also grows increasingly. In order to save users the time to find their favorite music among many music types, the music recommendation service arises at the historic moment and is widely concerned by scholars. Traditional music recommendation system based on the collaborative filtering algorithm has low recommendation accuracy, poor real-time performance, sparsity, system cold start, and so on. Moreover, the traditional music recommendation algorithm only simply uses user behavior characteristics and does not make good use of user history for listening to audio characteristics. In view of the above question, this section based on the attention mechanism of the deep neural network music recommendation algorithm, through the use of improved MFCC audio data preprocessing, the extracted audio combined with the user’s own portrait features, through the AIN RNN network recommended list, by learning user history listening to songs, improves the model-recommended accuracy.

Innovative Construction of Reinforcement Learning Model for Information Fusion in Music Education

In order to improve the effect of modern music education, this paper applies the digital information technology of music resources to the construction of the music teaching system and derives two new types of semi-decision-making process reinforcement learning algorithms based on the Bellman optimality equation base on discrete time. Moreover, this paper uses the comparative research methods to obtain the Q-value learning curve of the incremental value iterative reinforcement learning algorithm based on the semi-Markov decision process and the incremental value iterative reinforcement learning algorithm based on the dichotomy to improve the fusion effect of music teaching resources. Finally, this paper combines the actual needs of modern music education to construct an intelligent music teaching model.

Constructing a Music Network Teaching System by Using Neural Network Model with Wireless Audio Transmission

The purpose is to solve the problems that the traditional teaching methods limit the openness and extension of the music classroom, the interaction between teachers and students, the environment of students’ autonomous learning, and the music teaching situation. Wireless local area networks, Bluetooth, and intelligent transmission channels based on specific frequency can replace wired audio transmission and are widely used in the digital music classroom. Moodle system is used to build a music teaching network system based on the analysis of previous studies and the existing music teaching network platform. The system combines with Convolutional Neural Network (CNN) structure based on cloud computing to effectively identify and create music scores. The system effectiveness is further proved by analyzing the learning effect of the students and teaching effect of teachers in the conservatory of music. The results show that the system makes the experience of teachers and students in the teaching system different from before, and students can freely choose the time and place of class. In addition, the teaching method is flexible, and the teaching methods and resources are real-time. Therefore, in the music teaching network course, it overcomes some shortcomings of the traditional teaching mode. The music class is open and flexible. Teachers and students can have more interactive behavior and realize the students’ self-study and music teaching environment. This exploration can provide a theoretical basis and practical experience for music-related teaching.

Psychology Education Reform and Quality Cultivation of College Music Major From the Perspective of Entrepreneurship Education.

With the continuous development of the times, music education in primary and secondary schools is constantly innovating in terms of teaching concepts, teaching methods, and implementation methods. The reform of basic music education has aroused the reflection of music teacher education in colleges and universities, and the talent training model of music teachers has become a hot issue that has been widely concerned in the new era. To explore the educational significance and influencing factors of entrepreneurship education in college music education and examine the mediating role of self-efficacy and gender adjustment in entrepreneurial education, based on the analysis of music education and entrepreneurial talent training in colleges and universities, a questionnaire survey, statistics, and data processing were adopted in this work. In this work, a path model was established based on the mediating effect and the moderating effect, and the equation model was tested. The results show that music education has a positive correlation with entrepreneurial self-efficacy and a negative correlation with entrepreneurial intention, which has not been proved to have a certain effect on entrepreneurial intention. Entrepreneurial self-efficacy has a positive correlation with entrepreneurial intention, and the mediating effect between music professional education and entrepreneurial intention has also been proved. Since there are gender differences and certain stereotypes that cannot be ignored in music education learning, gender adjustment also has a certain influence on entrepreneurial self-efficacy based on music professional education, thereby having a moderating effect on entrepreneurial intentions. Entrepreneurship education can improve the comprehensive competitiveness of students. As a result, entrepreneurship education can improve the innovation and entrepreneurship education model and the music education system in colleges and universities by combining with the relationship between entrepreneurial efficacy and intention, and it contributes to the overall cultivation of talent in colleges and universities.

Applying Meta-Learning and Iso Principle for Development of EEG-Based Emotion Induction System.

Music is often used for emotion induction. ince the emotions felt when listening to it vary from person to person, customized music is required. Our previous work designed a music generation system that created personalized music based on participants' emotions predicted from EEG data. Although our system effectively induced emotions, unfortunately, it suffered from two problems. The first is that a long EEG recording is required to train emotion prediction models. In this paper, we trained models with a small amount of EEG data. We proposed emotion prediction with meta-learning and compared its performance with two other training methods. The second problem is that the generated music failed to consider the participants' emotions before they listened to music. We solved this challenge by constructing a system that adapted an iso principle that gradually changed the music from close to the participants' emotions to the target emotion. Our results showed that emotion prediction with meta-learning had the lowest RMSE among three methods (p < 0.016). Both a music generation system based on the iso principle and our conventional music generation system more effectively induced emotion than music generation that was not based on the emotions of the participants (p < 0.016).

Sacred Foundations of Folk Song Art

The purpose of the research is to determine the influence on the spontaneous and creative formation of song symbolism (in particular, musical-typological), which took place through instincts. The research methodology uses scientific methods to emphasise that folklore is an extremely complex and much more difficult system to be systematized than literature and music. Therefore, the criteria of genre classification of written works to folklore can be used in part, aware of their conditionality. In folklore, which is the bearer of the pre-written experience of mankind, in addition to mental formative and aesthetic achievements, biological instincts are also reflected. Instincts influenced the intuitive and creative formation of song symbolism (in particular, musical and typological). The scientific novelty of the research is that it emphasizes the folklore to be a legacy of the Neolithic worldview (phenomena: magical, religious, organizational and social, legal – like a wedding). For several thousand years under the influence of historical circumstances, and then Christianity in the Neolithic and folklore heritage has undergone significant distortions. The most significant changes have affected the vocabulary of lyrics and melodies of songs: they are the least stable components of the song in the race of time. Much less time affected the rites, beliefs and especially musical forms of songs. The rhythmic structure of musical forms has been preserved the most, as well as the compositional structure of song texts, which is always organically combined with musical rhythmics. Conclusions. The stability of ritual actions over the millennia, which is inherent in all religions (and paganism as well), is due primarily to the influence of sacredness. But sacredness does not affect the mind, but the senses. In order to assert it, the magic of the cycle of the agricultural year and the linear movement of human life (from the cradle to the grave) was worked out. To consolidate in the minds of the genus (tribe) through magic the laws of the annual agrarian cycle, the Magi-priests intuitively developed a perfect musical system of cognitive signs: winter, spring, and summer. The same applies to family rites. In fact, ritual melodies have clearly defined rhythmic structures that are associated with a certain time of year, with family events.

Tonal structures benefit short-term memory for real music: Evidence from non-musicians and individuals with congenital amusia

Congenital amusia is a neurodevelopmental disorder of music processing, which includes impaired pitch memory, associated to abnormalities in the right fronto-temporal network. Previous research has shown that tonal structures (as defined by the Western musical system) improve short-term memory performance for short tone sequences (in comparison to atonal versions) in non-musician listeners, but the tonal structures only benefited response times in amusic individuals. We here tested the potential benefit of tonal structures for short-term memory with more complex musical material. Congenital amusics and their matched non-musician controls were required to indicate whether two excerpts were the same or different. Results confirmed impaired performance of amusic individuals in this short-term memory task. However, most importantly, both groups of participants showed better memory performance for tonal material than for atonal material. These results revealed that even amusics’ impaired short-term memory for pitch shows classical characteristics of short-term memory, that is the mnemonic benefit of structure in the to-be-memorized material. The findings show that amusic individuals have acquired some implicit knowledge of regularities of their culture, allowing for implicit processing of tonal structures, which benefits to memory even for complex material.

Epidemiology and Risk Factors for Hearing Damage Among Adults Using Headphones via Mobile Applications.

BackgroundExposure to recreational noise is an emerging public health problem leading to hearing loss. Young people often spend their leisure and relaxation time listening to unsafe levels of music on their personal audio systems. This study aimed to study hearing impairment among young adults due to exposure to various audio gadgets.MethodologyThis cross-sectional study was conducted among 241 patients and healthcare workers aged 20-40 years. The data collection instrument was a questionnaire including the history and patterns of usage of headphones. A mobile application-based hearing test, a validated Smartphone hearing screening technology, by the World Health Organization (hearWHO) was used to check hearing.ResultsOf the 241 adult participants (aged 20 to 40 years), 188 (78%) were males and 53 (22%) were females. The mean hearWHO score was 46.02 ± 9.854, ranging from 21 to 85. The mean hearWHO score for headphone usage with a music system of 50.45 ± 12.32 (p = 0.023) and television of 44.13 ± 8.595 (p = 0.015) was statistically significant. The mean hearWHO score for daily headphone users was 45.93 ± 9.67, for users using daily for more than two hours was 46.59 ± 10.34, for those using headphones for leisure was 47.51 ± 7.74, and for usage music/gaming was 48.71 ± 12.57. Subclinical hearing loss was seen in 201 (83.4%) headphone users. A higher proportion of subclinical hearing loss (74.1%) was noted among participants who used headphones for multiple reasons such as leisure, education, service, music, and gaming.ConclusionsIn this study, a high proportion of headphone users were found to have subclinical hearing loss. This study generated imperative facts for people and emphasize that they look after their hearing. This study introduces the applicability of new technology in an Indian setting where hearing healthcare is facing challenges.

Design and Implementation of Multiple Music System Based on Internet of Things.

With the rapid development of social economy and Internet of Things, the society has entered the era of networking, digitalization, and intelligence, bringing great convenience to people's life; Internet of Things music system also has begun to get people's extensive attention. Due to the influence of such factors as strong music professionalism, complex music theory knowledge, and diverse changes, it is difficult to identify music features. In order to strengthen the user's personal experience of the music system, the multimusic systems are interconnected through information technology to realize the connection between objects and people. The system uses an embedded processor to realize the central control module and then according to network standard the sensor network is built, through radio frequency identification (RFID) technology for light, sound, infrared sensor, temperature, and other sensors for information reading. Music selection logic is designed based on the theory of music psychology and user behavior log, so as to select the best music for users to improve their mood and improve their life quality and work and study efficiency. At the same time, the system uses voice recognition technology to enhance user interaction, through the system, to provide the website to share their own music data and comments on songs and view song information, and the system runs stably and can collect high quality music signals and correctly identify the characteristics of music form and emotional characteristics.

Innovation and Discrete Dynamic Modeling of College Music Teaching Model Based on Multiple Intelligences Theory

The development of music teaching mode in colleges and universities needs to take music as the main body and carrier to spread and inherit the music theory system. With the continuous innovation and development of science and technology, the teaching mode and teaching system have also ushered in new changes. How to let students understand the process of music teaching and better appreciate the charm of music is the main problem faced by educators. Faced with the above situation, starting from the theory of multiple intelligences, this paper studies the innovation and discrete dynamic modeling of music teaching mode in colleges and universities. Firstly, this paper discusses the application effect of this method in music teaching based on the theory of multiple intelligences. This paper investigates the actual development of multiple intelligences theory in college music teaching. Combined with the characteristics of multiple intelligences theory, modeling and analysis of students’ interest changes in the intelligent music education model represented by the space vector model are carried out. Finally, this paper studies the discrete dynamic modeling of students’ learning effect after the optimization and innovation of music teaching mode in colleges and universities under the theory of multiple intelligences. The results show that in the innovation of music teaching mode, personalized learning services should be provided based on students’ interests. The theory of multiple intelligences can help teachers to effectively analyze the diversity characteristics and changes of students in teaching activities, and it is of great help to improve students’ musical performance.

Digital Music Recommendation Technology for Music Teaching Based on Deep Learning

With the rapid development of music streaming media service industry, users can easily hear any song on mobile devices. Internet has become a huge music storage platform. With the development of network and large-scale digital music industry, the acquisition and listening of music are presented to users in a more convenient way. How to find the music loved by users from the massive Internet digital music data has become the key problem and main goal to be solved in the field of music information retrieval. Personalized music recommendation system can accurately find and push songs that users may be interested in from tens of millions of huge music libraries according to users’ information under the condition that users only have vague demand for listening to songs. Relying on the traditional search method to find the music that you are interested in can no longer meet the needs of users, so the current music recommendation system needs to dig out the music that has no clear needs in the long tail to help people find their favorite songs.

Design of Vocal Music Teaching System Platform for Music Majors Based on Artificial Intelligence

The cyberspace consisting of information technology, artificial intelligence (AI), communication systems, computer systems, automatic control systems, digital devices, and the applications, services, and data they carry is advancing in an unprecedented way. It is no longer a dream to assist the development of pedagogical expert systems to solve the current problems of insufficient teachers and single teaching tools in music pedagogy reform and to realize the sharing of teaching resources. Artificial intelligence system under music education and education not only breaks the traditional music education model but also promotes the development of music education. This paper introduces the principle of artificial intelligence and analyzes the problems faced by university music education, and the implementation method of vocal education system based on artificial intelligence for music majors is also specifically studied in this paper. This mainly involves the design of the system architecture, such as the system architecture, which is designed to meet the requirements of the whole system. A survey of 100 students found that 82% of students preferred computer-based education to traditional music education. An artificial intelligence-based vocal teaching system for music majors is a system with good applications, where teachers keep track of students’ learning through reasoning and analysis, enabling students to learn on their own. Learning music requires constant practice, expanding horizons, and accepting new ways of teaching, and the combination of music and the Internet is the latest option to use resources wisely, optimize them, and provide comprehensive resources. Although a variety of music education aids have emerged, few are actually suitable for music classroom education, so it is important to study and implement AI-based speech education systems for music majors.

Remote Music Teaching Classroom Based on Machine Learning and 5G Network Station

Most recently, the technological impacts are increasing day by day. Artificial intelligence, machine learning, deep learning, and big data are used to acquire enough application feasibility to reduce work pressure for humans. The way information society is being developed in this world will result in some changes in the development of the education system. The impact will result in some changes in the development of the education system. This research focuses on improving remote music teaching following the presence of a 5G network. Data perception handling is considered the central issue in remote communication, which creates some inabilities to achieve better communication between the systems. To avoid this kind of risk management, most schools and colleges have planned to create a separate network working under wireless connections while delivering the course content to a minimum number of students. Another effective integration of this evolution is efficiently building the remote music education system. Here the compatibility should match both the cases, be they students or the teachers. In this case, network speed is considered one of the most significant impacts on students listening to online classes. Therefore, a 5G network might be the best path to take by increasing the network speed and giving a strong impetus. The proposed system utilizes convolutional neural network (CNN) algorithm to train the intelligent system to provide remote music education through wireless 5G networks. The parameters used for analyzing the proposed system with the existing system are compline, uncertainty, unconfirmed, and out-of-place. The proposed algorithm has achieved an overall accuracy of 99.13% than the existing system.

Examination of Neutral Intervals and Parent Scales in Persian Art Music: A Step toward the Standardization of the Musical System

Neutral intervals between minor and major intervals have been used in Persian/Iranian art music for centuries or even millennia. During the medieval era in the Muslim world, several scholars proposed ratios for the various intervals used in their recognized musical species and presented systematized parent scales. However, this did not result in unanimously accepted or standardized neutral interval sizes or fretting systems in practice, either in the past or in modern times. This article examines neutral intervals and parent scales in Persian art music in four sections. First, the neutral intervals according to the main medieval theorists of the Muslim world (specifically, from the eighth through the fifteenth centuries) are reviewed. Second, the opinions of several twentieth-century mu sicologists and their measured/proposed neutral intervals are critically examined. Third, several ratios for neutral seconds are considered based on different theoretical approach es, and a unique ratio, close to the current practice, is proposed. Finally, the arithmetic mean calculation method is directly applied to an 18-tone scale (for the fretting of a Persian tār/sitār) as an example of a standardized parent scale.

Analysis on the Application of Internet Multimedia Technology in College Music Education

In order to improve the effect of college music teaching, this paper combines Internet multimedia technology to construct a college music intelligent teaching system and uses the DASH multihypothesis framework to improve the transmission effect of Internet multimedia. Moreover, this paper manages the teaching resources generated in the process of music teaching and collects these resources into the library. The dynamic addition and editing of classifications are realized in the background, which is convenient for easy expansion and flexible adjustment of classifications. In addition, this paper combines the actual needs of music teaching in colleges and universities to construct the system function and get the system frame structure and work flow. Finally, this paper verifies the system of this paper in conjunction with experiments. The analysis shows that Internet multimedia technology has good effects in college music education, can effectively handle various resources of college music teaching, and can effectively improve the efficiency of college music education.

Collaborative Filtering-Based Music Recommendation in Spark Architecture

The use of recommendation algorithms to recommend music MOOC resources is a method that is gradually gaining ground in people’s lives along with the development of the Internet. The often used ALS collaborative filtering algorithm has an irreplaceable role in personalised recommender systems via the Spark MLlib platform. In the study, it is investigated how Spark can be used to implement efficient music system recommendations. The collaborative filtering algorithm based on the ALS model in the Spark architecture is currently the most widely used technique in recommendation algorithms, allowing for the analysis and optimisation of computational techniques. The project-based collaborative filtering algorithm used in the article enables the recommendation of music by avoiding personal information about the user. More accurate user recommendations are achieved by predicting the user’s preferences and focusing on the top ranked and highly preferred music recommendations. The method improves the performance of the recommendation algorithm, which is optimised by Spark shuffle on top of resource optimisation, and its performance improved by 54.8% after optimisation compared to when there is no optimisation.