A Fuzzy Multicriteria Assessment Mechanism towards Musical Courses Using Deep Learning

Abstract

Nowadays, the musical courses have been quite prevalent spiritual activities in online or offline scenarios. However, the teaching quality is diverse and cannot be easily assessed by general nonprofessional audience. Limited by the amount of experts, it is supposed to investigate intelligent mechanisms that can automatically assess the teaching quality of musical courses. To deal with such issue, the combination of artificial intelligence and conventional music knowledge acts as a promising way. In this work, a fuzzy multicriteria assessment mechanism is used towards musical courses with the use of a typical deep learning model: convolutional neural network (CNN). Specifically, note that features inside the musical symbol sequences are expected to be extracted by residual CNN structure. Next, multilevel features inside the musical notes are further fused with neural computing structure, so that feature abstraction of initial musical objects can be further improved. On this basis, notes can be identified with use of bidirectional recurrent unit structure in order to speed up fitting efficiency of the whole assessment framework. Comprehensive experimental analysis is conducted by comparing the proposed method with several baseline methods, showing a good performance effect of the proposal.

Music Tone Synthesis based Anti-Interference Dynamic Integral Neural Network optimized with Artificial Hummingbird Optimization Algorithm

“…The variety of musical characteristics is increasing with the growth of music information retrieval technology. Current music recognition technology primarily consists of a basic fusion of all musical features, which falls short of the high technical requirements for music appreciation [9][10][11]. The next research area for the existing music appreciation model is feature selection, screening, and combination [12].…”