Training Strategy of Music Expression in Piano Teaching and Performance by Intelligent Multimedia Technology

Abstract

Teaching using a multimedia technology in the ${21}^{st}$ century affords the possibility of developing novel instructional strategies and paves the way for the all-around extension of musical educational functions. The importance of multimedia teaching technology in piano instruction has started to emerge in our country and society due to the ongoing development of this kind of technology in music educational institutions here. The conventional method of teaching piano has several drawbacks that may be mitigated by using one of the several alternative methods of instruction for the instrument, especially in light of the ongoing advancements in science and technology. A pianist’s methods of expression are the tools they use to convey their thoughts and emotions about a piece of music to the audience. Teachers may demonstrate their musical skills to students and they must immediately focus on a musical expression which is vital for performers. In this paper, the Multimedia-based Piano Teaching Model (MPTM) has been proposed to improve the piano teaching quality. Traditional piano instruction is improved and developed using multimedia technology in this article. The Internet education model is used for teacher assessment, and the systematic way representing piano teaching combines different music educational materials. It begins building a sufficiently broad music network infrastructure resource sharing framework and benefits society’s amateur music literacy. The use of machine learning in students’ concrete piano instruction has the potential to thoroughly promote contemporary piano instruction and enhance the overall quality of instruction. To begin, an explanation of the intelligent piano’s features and capabilities is provided. The neural network is used to suggest a technique for detecting a piano note on a set. The network can assess the input piano music signal’s time frequency by translating the original time-domain waveform into the time-varying frequency distribution. Intelligent piano instruction analysis can effectively achieve the overall optimization of piano performance. The test results show that MPTM has a significant role in boosting the desire to learn to play the instrument. The experimental results show that the proposed MPTM achieves a learning skills ratio of 97.6%, a learning activity ratio of 98.5%, a student performance ratio of 93.8%, a teaching evaluation ratio of 90.3%, and a learning behavior ratio of 94.2% when compared to other methods.