A Hybrid Intelligence-Based Integrated Smart Evaluation Model for Vocal Music Teaching

Abstract

The smart evaluation for teaching effect has received much attention, especially in field of vocal music. Currently, such evaluation mainly relies on expert rating, which costs much human labors. Fortunately, the machine learning and deep learning-based techniques have been applied to evaluation affairs in a number of areas. This work takes the vocal music teaching as the main object, and introduces several typical intelligent algorithms to construct a smart evaluation workflow. Thus in this paper, a hybrid intelligence-based integrated smart evaluation model for vocal music teaching is proposed. First of all, a comprehensive evaluation system is formulated from mechanism as the main feature space. Then, convolutional neural network, long short-term memory network and multi-layer perceptrons are employed to establish a novel integrated structure as the main evaluation model. To assess the proposed technical framework in this paper, a case study is conducted and some simulation experiments are carried out for this purpose. The experimental results show that the proposal can well realize automatic evaluation for vocal music teaching.

Keywords

• Hybrid intelligence
• smart evaluation
• neural networks
• intelligent algorithms