Quantitative analysis of piano performance proficiency focusing on difference between hands

Kim, Sarah; Park, Jeong Mi; Rhyu, Seungyeon; Nam, Juhan; Lee, Kyogu

doi:10.1371/journal.pone.0250299

Cited by 9 publications

(14 citation statements)

References 70 publications

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“…The Kaplan–Meier survival curves were carried out to assess the survival time between high- and low-risk score RCC patients ( Lawrie et al, 1982 ). We validated the accuracy of the optimal cut-off value by the principal component analysis ( Kim et al, 2021 ). The Human Protein Atlas database ( https://www.proteinatlas.org/ ) was explored to investigate the expression level of the prognostic genes.…”

Section: Methodsmentioning

confidence: 99%

Molecular Characterization, Tumor Microenvironment Association, and Drug Susceptibility of DNA Methylation-Driven Genes in Renal Cell Carcinoma

Wang

Zhang

Hou

et al. 2022

Front. Cell Dev. Biol.

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Accumulating evidence suggests that DNA methylation has essential roles in the development of renal cell carcinoma (RCC). Aberrant DNA methylation acts as a vital role in RCC progression through regulating the gene expression, yet little is known about the role of methylation and its association with prognosis in RCC. The purpose of this study is to explore the DNA methylation-driven genes for establishing prognostic-related molecular clusters and providing a basis for survival prediction. In this study, 5,198 differentially expressed genes (DEGs) and 270 DNA methylation-driven genes were selected to obtain 146 differentially expressed DNA methylation-driven genes (DEMDGs). Two clusters were distinguished by consensus clustering using 146 DEMDGs. We further evaluated the immune status of two clusters and selected 106 DEGs in cluster 1. Cluster-based immune status analysis and functional enrichment analysis of 106 DEGs provide new insights for the development of RCC. To predict the prognosis of patients with RCC, a prognostic model based on eight DEMDGs was constructed. The patients were divided into high-risk groups and low-risk groups based on their risk scores. The predictive nomogram and the web-based survival rate calculator (http://127.0.0.1:3496) were built to validate the predictive accuracy of the prognostic model. Gene set enrichment analysis was performed to annotate the signaling pathways in which the genes are enriched. The correlation of the risk score with clinical features, immune status, and drug susceptibility was also evaluated. These results suggested that the prognostic model might be a promising prognostic tool for RCC and might facilitate the management of patients with RCC.

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Section: Methodsmentioning

confidence: 99%

Molecular Characterization, Tumor Microenvironment Association, and Drug Susceptibility of DNA Methylation-Driven Genes in Renal Cell Carcinoma

Wang

Zhang

Hou

et al. 2022

Front. Cell Dev. Biol.

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“…(1) A segment of pitch in a chromosome increases or decreases n steps at the same time, and the variation range of n is set as [1,2,3], as shown in the following formula:…”

Section: Interactive Genetic Algorithm Optimization Modelmentioning

confidence: 99%

“…e parameters algorithm is developed as follows: e length of the design work is 50 notes. e length of the text is obtained from the dictionary looking for trees [2,5]. e magnitude of the IGA population is 10, the maximum evolutionary algebra is 50, the crossover probability is 0.8, and the correlation probability is 0.2.…”

Section: Interactive Genetic Algorithm Optimization Modelmentioning

confidence: 99%

“…e magnitude of the IGA population is 10, the maximum evolutionary algebra is 50, the crossover probability is 0.8, and the correlation probability is 0.2. Equation ( 19) λ is 20, β is 4, and formula ( 16) has a range of N oscillations [1][2][3]. e average in equation ( 19) is 4 because of 20, so the simulation yields 4 false positives.…”

Section: Interactive Genetic Algorithm Optimization Modelmentioning

confidence: 99%

“…Based on this, actively promoting the progress of the multicultural education model is to comply with the pace of the progress of the times. At the same time, carrying out multicultural education in college education activities can promote students to look at the music culture of each region and each nation with a broader vision, promote mutual reference and learning among all ethnic groups to a great extent, and strengthen cooperation and exchange among all ethnic groups [2]. (2) It is conducive to improving national quality and promoting the construction of a civilized and powerful country and a harmonious society.…”

Section: Introductionmentioning

confidence: 99%

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Self‐Psychological Control and Creativity of Music Creation in Multimusic Environment

2022

Journal of Environmental and Public Health

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This study aimed to solve the difficulties in the research of self-psychology, three executive controls, and creativity in multimusic environment. In today’s teaching and performance, the study of piano adaptation has practical significance in broadening artistic vision, improving performance technology, and training symphonic music thinking. A research method of self-psychological three executive controls and creativity generation for creation in multimusic environment is proposed. A computer composition algorithm is based on the hidden Markov model and interactive genetic algorithm. By integrating melody elements and rhythm into the traditional Markov model with only notes or rhythm as the unit, a new hidden Markov music prediction model is established, and the interactive genetic algorithm is used to optimize the music segments. Simulation results show that the algorithm can use a small dataset to quickly create music clips with certain melody logic and in line with users’ personality. It is proved that the method based on the hidden Markov model and interactive genetic algorithm can meet the research needs of self-psychological three execution control and creativity generation in multimusic environment.

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Evaluation Strategy of the Piano Performance by the Deep Learning Long Short-Term Memory Network

Chang

Peng

2022

Wireless Communications and Mobile Computing

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With the development of society and the progress of technology, the piano education industry has a large market. In view of the problem of high payment fees in the piano education industry, the scientific and automatic nature of piano performance evaluation has attracted people’s attention. However, since most of the piano performance evaluation schemes are based on rules, the continuity of the piano music and the accuracy of playing are ignored. Therefore, the purpose is to design a scientific piano performance evaluation scheme that can play a certain role in the sustainable development of the piano education industry. Firstly, long short-term memory in deep learning is explored. Secondly, the musical characteristics of piano performance are analyzed according to the musical instrument digital interface. The piano music features are extracted, and a long short-term memory-based musical instrument digital interface piano performance evaluation model is constructed. Finally, it analyzes the number of hidden layers implemented in the long short-term memory model for piano performance evaluation. The accuracy of piano performance evaluation under different models is analyzed. Under the bidirectional long short-term memory network model, different piano performance levels are evaluated to realize the study of piano performance evaluation strategies. Compared with the accuracy of the recurrent neural network and the long short-term memory model with different hidden layers, the bidirectional long short-term memory model has the highest test accuracy, with an average of 69.78%. When the hidden layer of the bidirectional long short-term memory model is 3, the loss function L value is the smallest, which is 0.11. Different levels of piano skills are evaluated, and the results of the systematic evaluation are consistent with the performance of different levels. This shows that the BLSM model is feasible for the piano performance evaluation strategy system. This study not only conducts an in-depth analysis of the deep learning long short-term memory model but also proposes a long short-term memory-based musical instrument digital interface piano performance evaluation model. Additionally, the flaws such as the incomplete consideration of musical continuity and expressiveness when evaluating piano performance pieces have been compensated. Finally, through different model validations, the bidirectional long short-term memory model is concluded with good accuracy in piano performance evaluation. These conclusions provide theoretical research and practical significance for the accuracy of piano performance evaluation.

show abstract

Quantitative analysis of piano performance proficiency focusing on difference between hands

Cited by 9 publications

References 70 publications

Molecular Characterization, Tumor Microenvironment Association, and Drug Susceptibility of DNA Methylation-Driven Genes in Renal Cell Carcinoma

Molecular Characterization, Tumor Microenvironment Association, and Drug Susceptibility of DNA Methylation-Driven Genes in Renal Cell Carcinoma

Self‐Psychological Control and Creativity of Music Creation in Multimusic Environment

Evaluation Strategy of the Piano Performance by the Deep Learning Long Short-Term Memory Network

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