Piano Education of Children Using Musical Instrument Recognition and Deep Learning Technologies Under the Educational Psychology

Li, Huizi

doi:10.3389/fpsyg.2021.705116

Cited by 8 publications

(7 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…One area where technology has been utilised is in piano education for children. Li (2021) conducted a study on piano lessons using deep learning technology and found that it had practical value. This suggests that technology could augment the learning experience for children in music education.…”

Section: Live Performance and Stage Technologiesmentioning

confidence: 99%

Intelligent Music Applications: Innovative Solutions for Musicians and Listeners

TABAK

2023

Uluslararası Anadolu Sosyal Bilimler Dergisi

View full text Add to dashboard Cite

The integration of artificial intelligence and machine learning into smart music applications presents new avenues for musical expression. These applications facilitate the creation of emotionally expressive compositions by analyzing and interpreting the emotions conveyed in music. Simultaneously, they connect musicians from different locations, enabling collaborative music production in real-time through cloud-based platforms, thus simplifying collaboration in music production. The purpose of this research is to provide insights into the relationship between music and technology, which is closely intertwined with production, distribution, and consumption patterns. Using document analysis as the research method, this study explores the potential benefits of integrating artificial intelligence and machine learning into the music industry, examining future models and use cases from various perspectives. Additionally, this study outlines the need for future research to improve artificial intelligence and machine learning algorithms, ensure their responsible and ethical application, and provide insights into new possibilities for musical innovation.

show abstract

Section: Live Performance and Stage Technologiesmentioning

confidence: 99%

Intelligent Music Applications: Innovative Solutions for Musicians and Listeners

TABAK

2023

Uluslararası Anadolu Sosyal Bilimler Dergisi

View full text Add to dashboard Cite

show abstract

“…Yu Zhao 1* , WenWen Gao 2 & ShanShan Ku 3 An ever-growing portion of the economy is dedicated to the field of education, intensifying the urgency of identifying strategies to secure the sector's enduring prosperity and elevate educational standards universally. This study introduces a model for enhancing games and optimizing data analysis within the context of early childhood education (ECE) majors, hinging on deep learning (DL).…”

Section: Optimization Of the Game Improvement And Data Analysis Model...mentioning

confidence: 99%

Optimization of the game improvement and data analysis model for the early childhood education major via deep learning

Zhao,

Gao,

2023

Sci Rep

View full text Add to dashboard Cite

An ever-growing portion of the economy is dedicated to the field of education, intensifying the urgency of identifying strategies to secure the sector’s enduring prosperity and elevate educational standards universally. This study introduces a model for enhancing games and optimizing data analysis within the context of early childhood education (ECE) majors, hinging on deep learning (DL). This approach aims to enhance the quality of instruction provided to ECE majors and refine the effectiveness of their professional pursuits. This study commences by examining the incorporation of DL technologies within the domain of ECE and delving into their fundamental underpinnings. Subsequently, it expounds upon the design philosophy underpinning ECE games operating within the framework of DL. Finally, it outlines the game improvement and data analysis (GIADA) model tailored to ECE majors. This model is constructed upon DL technology and further refined through the integration of convolutional neural networks (CNN). Empirical findings corroborate that the DL-CNN GIADA model achieves data analysis accuracy ranging from 83 to 93% across four datasets, underscoring the pronounced optimization prowess bestowed by CNN within the DL-based GIADA model. This study stands as an invaluable reference for the application and evolution of artificial intelligence technology within the realm of education, thereby contributing substantively to the broader landscape of educational advancement.

show abstract

“…Deeplearning4J mainly uses data parallelization solutions. Currently, Deeplearning4J supports two data parallelization strategies: a parameter synchronous averaging and a decentralized gradient sharing scheme [21]. e principle of data parallelization is shown in Figure 4.…”

Section: Backpropagation Algorithmmentioning

confidence: 99%

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

Liao

2022

Mobile Information Systems

View full text Add to dashboard Cite

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.

show abstract

Piano Education of Children Using Musical Instrument Recognition and Deep Learning Technologies Under the Educational Psychology

Cited by 8 publications

References 31 publications

Intelligent Music Applications: Innovative Solutions for Musicians and Listeners

Intelligent Music Applications: Innovative Solutions for Musicians and Listeners

Optimization of the game improvement and data analysis model for the early childhood education major via deep learning

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

Contact Info

Product

Resources

About