Keyboard instruments are important tools in the teaching of music. In a recent publication in the journal PLoS ONE, researchers proposed a convolutional neural network (CNN) model for system debugging. This model allows the teaching robot to evaluate students' visuals and movements while playing musical instruments and provide targeted instruction.
Study: Revolutionizing Keyboard Instrument Education with Teaching Robots and Deep Learning. Image credit: Generated using DALL.E.3
The current study uses the literature review technique to explore the status of keyboard instrument teaching in preschool education, identify problems, and provide possible remedies.
Background
Keyboard instrument education within colleges and universities is pivotal in the landscape of music education in China, and educational reforms play a vital role in its long-term development. The current study, authored by experienced music educators in these institutions, seeks to address the existing deficiencies in keyboard instrument education. The objectives of music education at the collegiate level have changed in recent years, with an emphasis on cutting-edge models and techniques to fulfil the changing needs of music students.
Research in keyboard instrument teaching encompasses diverse perspectives and innovative strategies to enhance pedagogy and drive educational reform. Initial studies underscore the vital role of keyboard instrument teaching in student learning and educational reform. Recent explorations delve into intelligent robots' role in education, particularly in keyboard instrument teaching. Recognizing current keyboard instrument education as monotonous, the current study explores performance-teaching robots and their potential applications. Utilizing the CNN model in deep learning, the research analyzes students' images and actions during keyboard instrument play.
Teaching robots in keyboard education
In the context of teaching keyboard instruments to preschool education majors, instructors must consider the individual circumstances and needs of each student. This personalized approach enables teachers to tailor their instructional resources and conditions to meet the specific requirements of each student. By doing so, teachers can effectively assist students in mastering staff notation, comprehending proper piano playing techniques, and proficiently identifying various timbres in musical scores.
In keyboard and instrument teaching, the integration of teaching robots, underpinned by CNNs, heralds a comprehensive and efficient learning platform. These robots offer real-time performance assessment, music learning assistance, feedback mechanisms, and progress tracking. In so doing, they empower students to enhance their playing techniques and ignite a profound passion for music. The operational model of these teaching robots was designed based on CNNs. This model commences by collecting a substantial dataset of piano performances, which is then transformed into note sequences, forming the basis for model training. A deep CNN architecture is adopted for this purpose, encompassing convolutional layers, pooling layers, and fully connected layers, effectively capturing local patterns and facilitating the comprehension of advanced musical expressions.
These teaching robots primarily focus on real-time performance assessment as students play keyboard instruments. They provide immediate feedback based on predetermined criteria, empowering students to improve their playing techniques. Additionally, they offer step-by-step guidance on specific musical pieces, track progress, and foster ongoing improvement.
Experimental results and analysis
The questionnaire survey was conducted within school premises and targeted preschool education students from four universities in a specific city. The teaching robot operated using a deep CNN model developed through deep learning on a terminal browser. Notably, the robot could be controlled without needing any signal, network, or physical connection. The development platform used a web-based application, offering interactive computing capabilities for various tasks. The platform allowed the real-time display of video data captured by the teaching robot's camera on web pages.
The questionnaire assessed various aspects of teaching, including teachers' teaching attitudes and abilities and students' satisfaction with teachers. It also explored students' perceptions of teachers' conscientiousness and responsibility during classes. The results of students' evaluations of teachers' attitudes and responsibilities show variations among different schools.
School C received the most positive feedback, with approximately 80 percent of students expressing satisfaction with their keyboard teachers' seriousness and responsibility. Conversely, School A had 45 percent of students expressing dissatisfaction in this regard, indicating room for improvement. Schools B and D also faced challenges, with over 20 percent of their students expressing low satisfaction levels.
Results show the overall satisfaction of students with the teaching robot-assisted keyboard instruction after its introduction. Most students expressed high satisfaction, with the highest satisfaction rate reaching 96 percent. School C had the highest level of satisfaction, followed by Schools B and D, indicating an improvement in students' overall satisfaction with teaching robot-assisted instruction compared to conventional methods.
The results demonstrate the teaching robot model's highly efficient performance, with a forward inference time of approximately 0.186 seconds. The recognition accuracy using the CNN dataset remains consistently high, reaching around 98 percent across various prediction sample conditions, meeting the system design requirements effectively.
Conclusion
In summary, the proposed teaching robot model shows promise for revolutionizing keyboard instrument education. It utilizes extensive performance data, employs Deep CNN architecture, and offers real-time assessment and music learning support. Addressing computational efficiency, resource utilization, and user experience challenges will be crucial for broader adoption. By overcoming these obstacles, the teaching robot could become a transformative addition to music education, fostering improved learning experiences and achievements for keyboard instrument learners.
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