Abstract
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.
Highlights
Music has become an important part of education
E task of developing and implementing an effective evaluation process for music education seems to be more abstract than producing evaluation methods in other areas of teaching
For educational leaders to make valid decisions about music education in our schools, more precise and accurate evaluation models will be required about the effectiveness and nature of music teacher evaluation
Summary
Music has become an important part of education. With the reform and development of music education and the introduction of foreign advanced teaching methods, music education as an important part of quality education has been paid more and more attention [1]. Music performance evaluation (MPE) is the process of identification, assessment, and modeling the impact of music on the human listener [3]. One of the initial works discovering the benefits of computer-aided evaluation techniques for music was accomplished by Allvin [5] He described the application of pitch detection to analyze errors in musical presentation and provide a positive response to the learners. Music teaching evaluation systems usually depend upon extracting prominent and standard audio features from voice signal and applying machine learning algorithms to describe the value of the performance. Motivated by the triumph of these music evaluation systems, the inspiration of this study is to discover the potential of machine learning in the form of Naıve Bayes classifier for the valuation of music performance.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
