Jiangxi University of Technology, Nanchang 330098, China.
Adamson University, Manila 0900, Philippines.
J Environ Public Health. 2022 Jul 16;2022:3443404. doi: 10.1155/2022/3443404. eCollection 2022.
With the continuous improvement of people's living standards, the requirements of music majors for their training standards are also increasing, which leads to the development of music training in the direction of intelligence. This paper discusses the problems of breathing, coordination, and muscle control ability in vocal training and puts forward a vocal training method based on dynamic adjustment factor and Monte Carlo algorithm to solve the difficult problem of vocal training for college students and understand the relationship between vocal training and exercise. Firstly, the sports training set and vocal pronunciation training set are constructed in the form of clustering, and the samples in the set are analyzed discretely to ensure that the samples conform to normal distribution; then, using the Monte Carlo algorithm analyzes the two sample sets and finds out the relationship between exercise and vocal training. Finally, according to breathing, coordination, muscle control ability, and other indicators, calculate the impact of exercise on vocal sound. MATLAB simulation shows that the method proposed in this paper can analyze the influence of exercise on vocal vocalization from the perspective of breathing, coordination, muscle control ability, and other indicators. The accuracy of judgment results is more than 95%, and the time is less than 1 min. All indicators are better than traditional vocal training methods (90%, 2 min), which shows the effectiveness of the method proposed in this paper.
随着人们生活水平的不断提高,音乐专业学生对训练标准的要求也在不断提高,这导致音乐训练朝着智能化的方向发展。本文讨论了声乐训练中呼吸、协调和肌肉控制能力方面的问题,并提出了一种基于动态调整因子和蒙特卡罗算法的声乐训练方法,以解决大学生声乐训练的难题,了解声乐训练与运动之间的关系。首先,以聚类的形式构建运动训练集和声乐发音训练集,并对集中的样本进行离散分析,以确保样本符合正态分布;然后,使用蒙特卡罗算法对这两个样本集进行分析,找出运动与声乐训练之间的关系。最后,根据呼吸、协调、肌肉控制能力等指标,计算运动对声乐声音的影响。MATLAB 仿真表明,本文提出的方法可以从呼吸、协调、肌肉控制能力等指标来分析运动对声乐发声的影响。判断结果的准确率均在 95%以上,用时均在 1min 以内,所有指标均优于传统的声乐训练方法(90%,2min),这表明了本文提出的方法的有效性。