Piano Online Teaching based on Neural Network Technology

Yang, Yaokun

doi:10.1145/3495018.3495437

Cited by 4 publications

(3 citation statements)

References 4 publications

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“…Arranging harmony for melody is a key part of music creation, and it is also an extremely di cult job [8][9][10]. Only a few professional musicians can do this job, using arti cial intelligence technology to analyze music and learn complex harmony theory, and nally automatically [11]. It can not only save a lot of labor costs, but also provide a reference for the composer, or even a new creative inspiration [12].…”

Section: Introductionmentioning

confidence: 99%

Interactive Teaching Based on Artificial Intelligence and Its Application in Improving National Music Appreciation Ability

Zhang

2022

Mathematical Problems in Engineering

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The limitations of the traditional interactive teaching model are gradually becoming apparent in the current music teaching process. Based on the interactive teaching theory of artificial intelligence, this paper constructs an evaluation model of ethnic music appreciation ability and conducts research by using a novel method of fusion of long- and short-term audio features. The model proposes the classical features describing the sound quality and the beat histogram as the feature of the long-term rhythm of the music to form a mixed feature; secondly, the representation method of the song style vector is proposed, and the quantitative problem of music teaching is solved. During the simulation process, the model adopts the popular Model-View-Controller (MVC) design pattern and Unified Modeling Language (UML) and is developed with the Java 2 Platform Enterprise Edition (J2EE) architecture. It realized the user login function of three identities of students, teachers, and system administrators, and the subsystem can complete the management of students’ personal information, browse related courseware information and other resource information, and download courseware and tutorials. The experimental results show that the average value of the students’ evaluation is 0.606; the average value of case is 0.5852; it also reduces the workload of later maintenance of the system.

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Section: Introductionmentioning

confidence: 99%

Interactive Teaching Based on Artificial Intelligence and Its Application in Improving National Music Appreciation Ability

Zhang

2022

Mathematical Problems in Engineering

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“…e training method of RNN is mainly to obtain training samples by playing the Minuet piano piece several times by two piano teachers and three students. e input data to the RNN are derived for each play [29,30]. e overall rhythmic feeling and level of playing expression are then assessed separately.…”

Section: E Rnn Trainingmentioning

confidence: 99%

Educational Evaluation of Piano Performance by the Deep Learning Neural Network Model

Liao

2022

Mobile Information Systems

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In recent years, the piano education industry has occupied a huge market. However, the automatic evaluation function of piano performance has shortcomings in existing piano education products. Deep learning (DL) algorithm and the recurrent neural network (RNN) structure can help in automatics evaluation function of the piano performance. This paper proposes a Musical Instrument Digital Interface (MIDI) piano evaluation scheme based on the RNN structure and the Spark computing engine using the Deeplearning4J DL framework. The Deeplearning4J framework can run on the Java Virtual Machine; therefore, the entire system does not require cross-platform development. The Spark distributed computing engine realizes parallelization in music data preprocessing, feature extraction, and model training. Combined with the training user interface (UI) provided by the Deeplearning4J, it can improve developmental efficiency. Additionally, the RNN parameters are analyzed. The results demonstrate that the error value of the three-layer RNN structure is smaller than other closest rivals’ techniques. In particular, few piano training institutions and MIDI website datasets are used as the basis, and the experimental samples are collected. The neural network is trained, and the performance of the evaluation model is tested. The results show that the evaluation outcomes of the designed performance evaluation model for the piano are fundamentally consistent with the real levels of the players with assured feasibility; after 3k times of the training periods, the error of the RNN model is close to 0.01 and the network converges.

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“…At present, the main process of the intelligent creation classi cation system in the eld of music information retrieval is to rst manually extract the music features, then train and model the classi er, and nally input the music features into the built model for intelligent creation classi cation. But now the manual music feature extraction technology has encountered a bottleneck [10][11][12][13]. As a new feature extraction technology, deep learning has achieved excellent performance in the elds of music element processing and natural language understanding.…”

Section: Introductionmentioning

confidence: 99%

Construction of Music Intelligent Creation Model Based on Convolutional Neural Network

Jing

2022

Computational Intelligence and Neuroscience

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The application of machine learning technology to intelligent music creation has become a very important field in music creation. The main current research on music intelligent creation methods uses fixed coding steps in audio data, which lead to weak feature expression ability. Based on convolutional neural network theory, this paper proposes a deep music intelligent creation method. The model uses a convolutional recurrent neural network to generate an effective hash code, first preprocesses the music signal to obtain a Mel spectrogram, and then inputs it into a pretrained CNN to extract from its convolutional layers. The network space details and the semantic information of musical symbols are used to construct the feature map sequence using selection strategy for the feature map of each convolutional layer, so as to solve the problem of high data feature dimension and poor recognition performance. In the simulation process, the Mel cepstral coefficient method (MFCC) was used to extract the features of four different music signals, and the features that could represent each signal were extracted through the convolutional neural network, and the continuous signals were discretized and reduced. The experimental results show that the high-dimensional music data are dimensionally reduced at the data level. After the data are compressed, the correct rate of intelligent creation is as high as 98%, and the characteristic signal distortion rate is reduced to 5% below, effectively improving the algorithm performance and the ability to create music intelligently.

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Piano Online Teaching based on Neural Network Technology

Cited by 4 publications

References 4 publications

Interactive Teaching Based on Artificial Intelligence and Its Application in Improving National Music Appreciation Ability

Interactive Teaching Based on Artificial Intelligence and Its Application in Improving National Music Appreciation Ability

Educational Evaluation of Piano Performance by the Deep Learning Neural Network Model

Construction of Music Intelligent Creation Model Based on Convolutional Neural Network

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