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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“…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%

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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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“…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%

Section: Introductionmentioning

confidence: 99%

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

Li¹

2022

Scientific Programming

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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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“…In the training phase, the system completes the following tasks by training sample scores with specific piano accompaniment styles, beats, tempos, and modes: identifying the original piano accompaniment patterns and converting them into the sound pattern element structure of the piano accompaniment stored in the database; collecting rhythm, K-MP, and other observations; Re-adjust various parameters according to the obtained two HMM chains [13,30,31]. e main flow of the training process is shown in Figure 7.…”

Section: E Training Process Of Automatic Accompanimentmentioning

confidence: 99%

Designing an Automatic Piano Accompaniment System using Artificial Intelligence and Sound Pattern Database

2022

Mobile Information Systems

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The rapid development of artificial intelligence has made various fields to have corresponding connections with it, and music plays an important role in daily life. One of the applications of artificial intelligence in the field of art is to use music generation algorithms to endow machines with the function of generating melody. This ability can provide certain assistance to musicians when composing music, so that music creators can inspire inspiration in the creative process. Researchers have done a lot of work in the automatic generation of music. The piano is widely used in the field of automatic accompaniment and has strong versatility. The main purpose of this paper is to design a piano-based automatic accompaniment system, to think of melody and harmony as a machine learning-like task. By training on a selected series of samples, a database of phonomorphic metastructures is constructed, to systematically collect the original piano accompaniment patterns by building a sound pattern database, and convert the collected original sound patterns into the original sound pattern structure and store in the database. Then, by establishing two Hidden Markov Model (HMM) systems to simulate the thinking mode of the composer’s piano accompaniment process, a melody style related to a certain collection of samples is formed. Finally, the Viterbi algorithm is used to select the appropriate piano accompaniment metastructure in the database to generate the piano accompaniment of the melody section. The experimental results of this paper show that, as far as the accompaniment generation effect is concerned, although the generation effect of this paper is slightly different, the overall difference does not change much. It shows that the effect generated by the method in this paper is relatively stable.

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

Cited by 35 publications

References 23 publications

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

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

Designing an Automatic Piano Accompaniment System using Artificial Intelligence and Sound Pattern Database

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