A decision-support system for assessing the function of machine learning and artificial intelligence in music education for network games

Yun, Zou Hong; Alshehri, Yasser; Alnazzawi, Noha; Ullah, Ijaz; Noor, Salma; Gohar, Neelam

doi:10.1007/s00500-022-07401-4

Cited by 19 publications

(3 citation statements)

References 24 publications

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“…It had practical reference value for promoting the heterogeneity of edge servers and the optimization of teaching resources. Hong et al (2022) [ 22 ] researched the role of machine learning and artificial intelligence in music education for online games through the optimization of decision support systems. The results showed that innovative and complex methods based on artificial intelligence and machine learning were being used to improve music teaching.…”

Section: Discussionmentioning

confidence: 99%

Intelligent auxiliary system for music performance under edge computing and long short-term recurrent neural networks

Wang

2023

PLoS ONE

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Music performance action generation can be applied in multiple real-world scenarios as a research hotspot in computer vision and cross-sequence analysis. However, the current generation methods of music performance actions have consistently ignored the connection between music and performance actions, resulting in a strong sense of separation between visual and auditory content. This paper first analyzes the attention mechanism, Recurrent Neural Network (RNN), and long and short-term RNN. The long and short-term RNN is suitable for sequence data with a strong temporal correlation. Based on this, the current learning method is improved. A new model that combines attention mechanisms and long and short-term RNN is proposed, which can generate performance actions based on music beat sequences. In addition, image description generative models with attention mechanisms are adopted technically. Combined with the RNN abstract structure that does not consider recursion, the abstract network structure of RNN-Long Short-Term Memory (LSTM) is optimized. Through music beat recognition and dance movement extraction technology, data resources are allocated and adjusted in the edge server architecture. The metric for experimental results and evaluation is the model loss function value. The superiority of the proposed model is mainly reflected in the high accuracy and low consumption rate of dance movement recognition. The experimental results show that the result of the loss function of the model is at least 0.00026, and the video effect is the best when the number of layers of the LSTM module in the model is 3, the node value is 256, and the Lookback value is 15. The new model can generate harmonious and prosperous performance action sequences based on ensuring the stability of performance action generation compared with the other three models of cross-domain sequence analysis. The new model has an excellent performance in combining music and performance actions. This paper has practical reference value for promoting the application of edge computing technology in intelligent auxiliary systems for music performance.

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Section: Discussionmentioning

confidence: 99%

Intelligent auxiliary system for music performance under edge computing and long short-term recurrent neural networks

Wang

2023

PLoS ONE

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“…Artificial intelligence (AI) is increasingly playing a significant role in Music teaching, revolutionizing the way educators and learners engage with the language [7]. AI-powered tools and platforms offer personalized learning experiences tailored to individual needs and proficiency levels [8].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Construction of University Music Education Teaching System Based on Artificial Intelligence Technology

2024

jes

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The university music education teaching system plays a vital role in nurturing aspiring musicians and music enthusiasts by providing a comprehensive framework for musical learning and development. This system typically encompasses a diverse range of courses, workshops, and performance opportunities designed to cultivate students' musical talents, theoretical knowledge, and practical skills. Through a combination of classroom instruction, ensemble rehearsals, private lessons, and hands-on experiences, students receive a well-rounded musical education that covers various genres, styles, and traditions. Moreover, university music education teaching systems often incorporate state-of-the-art facilities, including rehearsal rooms, recording studios, and performance venues, to support students' artistic growth and creative expression. This paper presents an innovative approach to the intelligent construction of university music education teaching systems, leveraging artificial intelligence (AI) technology with Intelligent Fuzzy Regression Classification (IFRC). Recognizing the complexity and diversity of music education, this research aims to optimize teaching methodologies and enhance learning outcomes through AI-driven strategies. The proposed approach integrates AI technology, particularly IFRC, into the design and implementation of university music education teaching systems. IFRC combines fuzzy logic with regression analysis and classification techniques to model and predict complex relationships within music education datasets, enabling the system to adapt and respond dynamically to student needs and preferences. The IFRC-enhanced teaching system can generate personalized learning pathways, recommend tailored resources, and provide real-time feedback to students and instructors alike. This intelligent adaptation to individual learning styles and progress fosters a more engaging, effective, and inclusive music education environment. The IFRC-enhanced teaching system achieves an average improvement of 25% in student performance compared to traditional teaching methods. Moreover, specific musical skills, such as sight-reading, ear training, and music theory comprehension, exhibit notable enhancements, with average score increases of 30%, 20%, and 35%, respectively.

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“…Hong Yun Zou explored the function of AI technology and other similar technologies in the music education of games. First, he proposed a decision support system to realize this function, and then through the in-depth study of AI related technologies Analysis, to determine the effectiveness of the combination of music teaching and AI technology [6]. Kayis Mert used AI technology to classify classical music in a certain area, and then analyzed the local music education model in turn.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence Technology in the Assessment of Teachers' Music Teaching Skills Training

2023

IJEIS

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The development of modern technology and economy has fully improved people's daily life, and at the same time, the disposable income of residents has also increased rapidly. At this time, people began to pay more and more attention to art education such as music for the next generation. Education. At this time, the education and training enterprises of music and various art disciplines have a broader development market, and at the same time, the improvement of the market competition environment has also made the training mode used by various training enterprises to train the teaching skills of music teachers there are bigger deficiencies. At this time, the rise and rapid progress of artificial intelligence (AI) technology has also brought more ideas and development opportunities for the training of music teachers' professional skills. Relevant companies have begun to pay attention to the relationship between AI technology and teachers' music teaching skills training. This combination can not only fully improve teachers' music teaching skills, but also greatly shorten the period required for music teacher training. In this paper, through the in-depth exploration of AI technology, a comprehensive model for the training and assessment of music teachers' professional teaching skills based on AI technology is proposed to provide efficient training for relevant teachers' majors, so that Enable related enterprises to achieve higher quality development. At the same time, this AI technology-based training and assessment mode for teachers' music teaching skills also greatly improves the professionalism of teachers, thereby producing better teaching effects.

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A decision-support system for assessing the function of machine learning and artificial intelligence in music education for network games

Cited by 19 publications

References 24 publications

Intelligent auxiliary system for music performance under edge computing and long short-term recurrent neural networks

Intelligent auxiliary system for music performance under edge computing and long short-term recurrent neural networks

Intelligent Construction of University Music Education Teaching System Based on Artificial Intelligence Technology

Artificial Intelligence Technology in the Assessment of Teachers' Music Teaching Skills Training

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