Evaluation of music teaching is a highly subjective task often depending upon experts to assess both the technical and artistic characteristics of performance from the audio signal. This article explores the task of building computational models for evaluating music teaching using machine learning algorithms. As one of the widely used methods to build classifiers, the Naïve Bayes algorithm has become one of the most popular music teaching evaluation methods because of its strong prior knowledge, learning features, and high classification performance. In this article, we propose a music teaching evaluation model based on the weighted Naïve Bayes algorithm. Moreover, a weighted Bayesian classification incremental learning approach is employed to improve the efficiency of the music teaching evaluation system. Experimental results show that the algorithm proposed in this paper is superior to other algorithms in the context of music teaching evaluation.

中文翻译：

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基于加权朴素贝叶斯的音乐教学评价模型构建

音乐教学的评价是一项高度主观的任务，通常取决于专家从音频信号中评估演奏的技术和艺术特征。本文探讨了使用机器学习算法构建用于评估音乐教学的计算模型的任务。朴素贝叶斯算法作为目前广泛使用的构建分类器的方法之一，以其强大的先验知识、学习特征和较高的分类性能成为最流行的音乐教学评价方法之一。在本文中，我们提出了一种基于加权朴素贝叶斯算法的音乐教学评价模型。此外，采用加权贝叶斯分类增量学习方法来提高音乐教学评估系统的效率。