scholarly journals Toward Piano Teaching Evaluation Based on Neural Network

2022 ◽  
Vol 2022 ◽  
pp. 1-9
Author(s):  
Wanshu Luo ◽  
Bin Ning
Keyword(s):  
Neural Network ◽  
Music Performance ◽  
Evaluation Process ◽  
Piano Performance ◽  
Multimedia Software ◽  
Factors Affecting ◽  
Piano Teaching ◽  
Teacher Student ◽  
Piano Education ◽  
Performance And Evaluation

With the rise of piano teaching in recent years, many people participated in the team of learning steel playing. However, expensive piano teaching fees and its unique one-to-one teaching model have caused piano education resources to be very short, so learning piano performance has become a very extravagant event. The factors affecting music performance are varying, and there are many types of their evaluation such as rhythm, expressiveness, music, and style grasp. The computer is used to simulate this evaluation process to essentially identify the mathematical relationship between factors affecting music performance and evaluation indicators. The use of computer multimedia software for piano teaching has become a feasible way to alleviate the contradiction. This paper discusses the implementation method of piano teaching software, the issues of computer piano teaching, the computer teaching as one-way knowledge, and the lack of interaction. The neural network (NN) model is used to evaluate the piano performance and simulate teachers to guide students through their exercise. The performance of the proposed system is tested for the piano music of “Ode to Joy,” which is different from the collection of NN training samples, and is delivered ten times by another piano teacher, student A (piano level 6), and student B (piano level 5).

Piano playing teaching system based on artificial intelligence – design and research

2020 ◽  
pp. 1-9
Author(s):  
Meichen Liu ◽  
Jieru Huang
Keyword(s):  
Neural Network ◽  
Teaching Model ◽  
Multimedia Software ◽  
Teaching System ◽  
Piano Teaching ◽  
Teachers And Students ◽  
Piano Education ◽  
Computer Teaching ◽  
Network Studies ◽  
Piano Playing

In recent years, with the rise of piano teaching, many people began to learn to play the piano. However, the expensive piano teaching cost and its unique teaching model that teachers and students are one to one have caused the shortage of piano education resources, and people learn piano playing has become a luxury activity. The use of computer multimedia software for piano teaching has become a feasible way to alleviate this contradiction. This paper proposes the design of an intelligent piano playing teaching system based on neural network, studies the realization method of the piano teaching system, presents a method of evaluating piano playing by using neural network model for the difficulties in computer piano teaching, that is, computer teaching is one-way knowledge transfer without interaction. In addition, this paper simulates the teacher to guide the students to carry on the playing practice, which is of great significance to the teaching of the piano.

AI based research on exploration and innovation of development direction of piano performance teaching in university

2020 ◽  
pp. 1-7
Author(s):  
Yanjie Chen ◽  
Na Zheng
Keyword(s):  
New Media ◽  
Traditional Classroom ◽  
Piano Performance ◽  
Piano Teaching ◽  
Network Information ◽  
Development Direction ◽  
Teachers And Students ◽  
Piano Education ◽  
Piano Majors ◽  
Traditional Classroom Teaching

This paper investigates the cognition status of information piano education for teachers and students in a university, which mainly includes a summary of the piano teaching status in a university and make an analysis and summary of the investigation results. In addition, this paper puts forward the direction of the network information reform and construction for piano majors in Colleges and universities, mainly including three aspects, that is, taking piano “micro class” teaching to arm traditional classroom teaching, using the new media to build a networked piano learning environment, and building the piano teaching “MOOC” platform.

scholarly journals Determination of the Factors Affecting King Abdul Aziz University Published Articles in ISI by Multilayer Perceptron Artificial Neural Network

Mathematics ◽  
2020 ◽  
Vol 8 (5) ◽  
pp. 766
Author(s):  
Rashad A. R. Bantan ◽  
Ramadan A. Zeineldin ◽  
Farrukh Jamal ◽  
Christophe Chesneau
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Multilayer Perceptron ◽  
Research Study ◽  
Real Data ◽  
Factors Affecting ◽  
King Abdulaziz University ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Deanship of scientific research established by the King Abdulaziz University provides some research programs for its staff and researchers and encourages them to submit proposals in this regard. Distinct research study (DRS) is one of these programs. It is available all the year and the King Abdulaziz University (KAU) staff can submit more than one proposal at the same time up to three proposals. The rules of the DSR program are simple and easy so it contributes in increasing the international rank of KAU. The authors are offered financial and moral reward after publishing articles from these proposals in Thomson-ISI journals. In this paper, multiplayer perceptron (MLP) artificial neural network (ANN) is employed to determine the factors that have more effect on the number of ISI published articles. The proposed study used real data of the finished projects from 2011 to April 2019.

scholarly journals The Neural Network Assisted Land Use Regression

Atmosphere ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 452
Author(s):  
Jan Bitta ◽  
Vladislav Svozilík ◽  
Aneta Svozilíková Krakovská
Keyword(s):  
Neural Network ◽  
Land Use ◽  
Linear Regression ◽  
Dispersion Model ◽  
Land Use Regression ◽  
Emission Data ◽  
Pollution Dispersion ◽  
Factors Affecting ◽  
Standard Linear

Land Use Regression (LUR) is one of the air quality assessment modelling techniques. Its advantages lie mainly in a much simpler mathematical apparatus, quicker and simpler calculations, and a possibility to incorporate more factors affecting pollutant concentration than standard dispersion models. The goal of the study was to perform the LUR model in the Polish-Czech-Slovakian Tritia region, to test two sets of pollution data input factors, i.e., factors based on emission data and pollution dispersion model results, to test regression via neural networks and compare it with standard linear regression. Both input datasets, emission data and pollution dispersion model results, provided a similar quality of results in the case when standard linear regression was used, the R2 of the models was 0.639 and 0.652. Neural network regression provided a significantly higher quality of the models, their R2 was 0.937 and 0.938 for the factors based on emission data and pollution dispersion model results respectively.

Research on the hydrographic survey cycle for updating navigational charts

2021 ◽  
pp. 1-13
Author(s):  
Jing Duan ◽  
Xiaoxia Wan ◽  
Jianan Luo
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Calculation Model ◽  
Impact Factors ◽  
Factors Affecting ◽  
Material Resources ◽  
Hydrographic Survey ◽  
Survey Planning ◽  
The Impact ◽  
The Relationship

Abstract Due to the vast ocean area and limited human and material resources, hydrographic survey must be carried out in a selective and well-planned way. Therefore, scientific planning of hydrographic surveys to ensure the effectiveness of navigational charts has become an urgent issue to be addressed by the hydrographic office of each coastal state. In this study, a reasonable calculation model of hydrographic survey cycle is established, which can be used to make the plan of navigational chart updating. The paper takes 493 navigational charts of Chinese coastal ports and fairways as the research object, analyses the fundamental factors affecting the hydrographic survey cycle and gives them weights, proposes to use the BP neural network to construct the relationship between the cycle and the impact factors, and finally establishes a calculation model of the hydrographic survey cycle. It has been verified that the calculation cycle of the model is effective, and it can provide reference for hydrographic survey planning and chart updating, as well as suggestions for navigation safety.

scholarly journals The Interpretive Shaping of Embodied Musical Structure in Piano Performance

2013 ◽  
Vol 8 (2) ◽  
pp. 92 ◽  
Author(s):  
Bryony Buck ◽  
Jennifer MacRitchie ◽  
Nicholas J. Bailey
Keyword(s):  
Music Performance ◽  
Structural Features ◽  
Upper Body ◽  
Musical Structure ◽  
Piano Performance ◽  
Body Movements ◽  
Motion Patterns ◽  
Structural Interpretation ◽  
Highly Skilled ◽  
A Performance

Research has indicated that the magnitude of physical expressive movements during a performance helps to communicate a musician's affective intent. However, the underlying function of these performance gestures remains unclear. Nine highly skilled solo pianists are examined here to investigate the effect of structural interpretation on performance motion patterns. Following previous findings that these performers generate repeated patterns of motion through overall upper-body movements corresponding to phrasing structure, this study now investigates the particular shapes traced by these movements. Through this we identify universal and idiosyncratic features within the shapes of motion patterns generated by these performers. Gestural shapes are examined for performances of Chopin’s explicitly structured A major Prelude (Op. 28, No. 7) and are related to individual interpretations of the more complex phrasing structure of Chopin’s B minor Prelude (Op. 28, No. 6). Findings reveal a universal general embodiment of phrasing structure and other higher-level structural features of the music. The physical makeup of this embodiment, however, is particular to both the performer and the piece being performed. Examining the link between performers' movements and interpreted structure strengthens understanding of the connection between body and instrument, furthering awareness of the relations between cognitive interpretation and physical expression of structure within music performance.

scholarly journals Modeling Judges’ Scores in Artistic Gymnastics

2019 ◽  
Vol 12 (1) ◽  
pp. 1-9
Author(s):  
Melanie Mack ◽  
Maximilian Bryan ◽  
Gerhard Heyer ◽  
Thomas Heinen
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Evaluation Process ◽  
Model Calculations ◽  
Kinematic Data ◽  
Complete Dataset ◽  
Exploratory Approach ◽  
Movement Kinematic ◽  
Alternative Approaches ◽  
The Relationship

Background: In artistic gymnastics, performance is observed and evaluated by judges based on criteria defined in the code of points. However, there is a manifold of influences discussed in the literature that could potentially bias the judges’ evaluations in artistic gymnastics. In this context, several authors claim the necessity for alternative approaches to judging gymnastics utilizing biomechanical methods. Objective: The aim of this study was to develop and evaluate a model-based approach to judge gymnastics performance based on quantitative kinematic data of the performed skills. Methods: Four different model variants based on kinematic similarity calculated by a multivariate exploratory approach and the Recurrent Neural Network method were used to evaluate the relationship between the movement kinematics and the judges’ scores. The complete dataset consisted of movement kinematic data and judgment scores of a total of N = 173 trials of three different skills and routines from women’s artistic gymnastics. Results: The results exhibit a significant relationship between the predicted score and the actual score for six of the twelve model calculations. The different model variants yielded a different prediction performance in general across all skills and also in terms of the different skills. In particular, only the Recurrent Neural Network model exhibited significant correlation values between the actual and the predicted scores for all three investigated skills. Conclusion: The results were discussed in terms of the differences of the models as well as the various factors that might play a role in the evaluation process.

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