Piano Performance and Music Automatic Notation Algorithm Teaching System Based on Artificial Intelligence

Yang, Yaokun

doi:10.1155/2021/3552822

Cited by 35 publications

(28 citation statements)

References 23 publications

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“…Hence, there is a need for an intelligent online piano teaching system in which children's ages, relevant pedagogical material, and educational psychology should all be taken into consideration when teachers devise scientific piano learning procedures [6]. Individualized piano instruction can be tailored to the needs of individual students by incorporating deep learning (DL) technology into the classroom, and the combination of children's psychological issues ensures that each child receives personalized attention [7]. In this paper, a DL algorithm based on a recurrent neural network is being utilized to produce a musical instrument recognition technology for use in children's piano lessons, with the goal of enhancing the intellectualization of piano training.…”

Section: Introductionmentioning

confidence: 99%

Study on Intelligent Online Piano Teaching System Based on Deep Learning Recurrent Neural Network Model

2022

Mobile Information Systems

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This study has been conducted to solve the problem of repetitive piano lessons and to bring a personalized experience for each piano learner. The application of deep learning (DL) technology for children’s piano teaching has a positive effect on their interest in the subject and improves the teaching quality. Music instruments were identified in the system using an instrument recognition model that was developed using deep learning techniques. It was also utilized to help children learn to play the piano by giving them direction and boosting their excitement for it. The proposed model’s ability to recognize and acquire features has been improved. The recurrent neural network (RNN) demonstrated instrument recognition accuracy of 96.4%, and the model’s recognition error rate decreased and stabilized as the number of iterations increased. The proposed RNN for musical instruments recognizes instruments by using DL to accurately identify musical properties.

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Section: Introductionmentioning

confidence: 99%

Study on Intelligent Online Piano Teaching System Based on Deep Learning Recurrent Neural Network Model

2022

Mobile Information Systems

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“…It can be seen that the gradient descent method is used here. Equation (6) shows the computation between each convolutional layer.…”

Section: E Design and Cnn Of Piano Teaching Scheme Integratingmentioning

confidence: 99%

“…e video method can record the ambient atmosphere, music atmosphere, and the content of the piano performance at the music scene, and it can effectively record the performance of the audience and the performer. Piano learners can learn more knowledge from piano-playing videos [6,7]. e image can e ectively record the key piano performance, it can capture the mood and performance content of the piano player very well.…”

Section: Introductionmentioning

confidence: 99%

Design and Application of the Piano Teaching System Integrating Videos and Images

Li¹

2022

Scientific Programming

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The art of piano playing has been continuously entering into people’s life. However, with the continuous improvement of science and technology and living standards, the traditional teaching mode can no longer meet the piano teaching mode. The teaching of piano is different from traditional subjects, such as Chinese and mathematics. It requires students to experience the artistic characteristics and the live atmosphere of the players brought by the piano. This study integrates video and image teaching methods with piano teaching. Videos and images can more intuitively show the live atmosphere brought by piano players and musical artistic features brought by the piano. At the same time, this study uses the convolutional neural network (CNN) method to study the relevant features of videos and images of piano teaching. These features are mainly the characteristics of piano music, the behavior of players, and the basic knowledge of a piano. The research results show that the clustering method can effectively classify the features of videos and images in piano teaching, and the maximum classification error is only 1.89%. The CNN method also has high performance in predicting the relevant features of piano teaching videos and images. Accuracy. The largest prediction error is only 2.23%, and the linear correlation coefficient also exceeds 0.95. This set of the piano teaching mode that combines videos and images is beneficial to both teachers and students.

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“…In recent years, the use of music technology in schoolcurricula has been transforming the way teachers and students communicate with each other in the music learning classroom, which has led to a subtle change in the way music classroom situations fit into the diverse cultures of society [15][16][17][18][19]. Users usually download suitable smart software from their computers or phones to assist artists in their learning.…”

Section: E Evolution and Current Research Status Of Ai In Musicmentioning

confidence: 99%

A Study of Information-Based Teaching Strategies for the Saxophone Based on Deep Learning

Cao

2022

Mobile Information Systems

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Education is one of the core elements in building the career of an individual. It needs proper strategies and techniques to fulfill the modern world’s requirements, such as intelligent learning systems, intelligent management systems, and intelligent computational systems. At present, there is a dearth of systematic debate on how to proceed along the road of machine learning (ML) and education. As a result, this study focuses on the use of artificial intelligence (AI) to promote saxophone informatization teaching strategies, particularly the new strategies brought by deep learning (DL) to saxophone teaching from the perspectives of teaching resources, teaching environment, teaching and learning strategies, teaching management, and teaching evaluation. A matrix decomposition strategy with dynamic weight learning is suggested by keeping the earlier aspects in consideration, which is used to produce a recommendation algorithm that fundamentally incorporates multiple contextual features such as geographic, temporal, and social characteristics, as well as the weight parameter learning process, and essentially constitutes the linear fusion technique’s building approach. All the experiments are carried out on the yelp dataset in order to check if the recommended algorithm is effective or not. The performance of the suggested method is compared to the benchmark algorithms in order to prove that the dynamic weight parameter learning technique is as effective as gradient descent. A comparison of the algorithm that employs one contextual element alone vs the method that uses three contextual factors is also conducted to demonstrate that the linear fusion of several components improves the system’s recommendation performance.

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Piano Performance and Music Automatic Notation Algorithm Teaching System Based on Artificial Intelligence

Cited by 35 publications

References 23 publications

Study on Intelligent Online Piano Teaching System Based on Deep Learning Recurrent Neural Network Model

Study on Intelligent Online Piano Teaching System Based on Deep Learning Recurrent Neural Network Model

Design and Application of the Piano Teaching System Integrating Videos and Images

A Study of Information-Based Teaching Strategies for the Saxophone Based on Deep Learning

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