Music Department, Normal College, Changshu Institute of Technology, Changshu 215500, China.
J Environ Public Health. 2022 Sep 1;2022:9251793. doi: 10.1155/2022/9251793. eCollection 2022.
With the rise of Erhu teaching in recent years, a large number of people have joined the team to learn Erhu playing. However, due to the high cost of teaching and the unique one-to-one teaching mode between teachers and students, Erhu education resources are very scarce. Learning Erhu performance has become a luxury activity. Nowadays, with the rise of artificial intelligence, computer music is developing rapidly. Music has two important aspects: composition and performance. Different kinds of instruments convey different styles, and players inject different rhythms and dynamics into their performance, thus producing rich expressive force. The development of image style conversion, which opens people's evaluation of music performance, is an important issue in many fields of artificial intelligence (it is also known as intelligence, machine intelligence, referring to the intelligence shown by the machine made by people. Usually, artificial intelligence refers to the technique of presenting human intelligence through ordinary computer programs). For an Erhu song, there are various factors that affect its effectiveness, and there are many indexes to evaluate it, such as sense of rhythm, expressive force, musical sense, style, and so on. Using a computer to simulate the evaluation process is essential to find out the mathematical relationship between the factors that affect the performance of music and the evaluation indexes. Neural network is a kind of mathematical model proposed by simulating the way of thinking of human brain in artificial intelligence. It has the advantages of not having strict requirements on data distribution, nonlinear data processing method, strong robustness, and dynamics and is very suitable for the mathematical model of evaluation system. In addition, the neural network also has a strong theoretical basis, and their application in various industries has developed basically mature. This paper tries to introduce a deep neural network mathematical model into the evaluation system of Erhu performance, and the experimental results prove the reliability and practicality of the method in this paper. It can provide a method basis and theoretical reference for evaluation of Erhu performance effect.
近年来，随着二胡教学的兴起，大量的人加入了学习二胡演奏的队伍。然而，由于教学成本高以及师生之间独特的一对一教学模式，二胡教育资源非常稀缺。学习二胡演奏已经成为一种奢侈的活动。如今，随着人工智能的兴起，计算机音乐正在迅速发展。音乐有两个重要方面：作曲和演奏。不同种类的乐器传达不同的风格，演奏者在演奏中注入不同的节奏和力度，从而产生丰富的表现力。图像风格转换的发展，即人们对音乐演奏的评价方式的发展，是人工智能（也称为智能、机器智能，是指人类制造的机器所表现出的智能。通常，人工智能是指通过普通计算机程序呈现人类智能的技术）许多领域的重要问题。对于一首二胡曲，有许多因素影响其效果，有许多指标来评价它，如节奏感、表现力、乐感、风格等。使用计算机来模拟评价过程对于找出影响音乐演奏的因素与评价指标之间的数学关系至关重要。神经网络是人工智能中模拟人类大脑思维方式提出的一种数学模型。它具有对数据分布没有严格要求、非线性数据处理方法、强鲁棒性、动态性等优点，非常适合评价系统的数学模型。此外，神经网络也具有很强的理论基础，它们在各个行业的应用已经基本成熟。本文尝试将深度神经网络数学模型引入二胡演奏评价系统中，实验结果证明了本文方法的可靠性和实用性。它可以为二胡演奏效果评价提供方法基础和理论参考。
