Abstract
In the contemporary landscape of diversified talent cultivation, enhancing education through intelligent means and expediting the process of talent development stand as paramount pursuits. Within the domain of instrumental music education, beyond merely listening to student performances, it becomes imperative to assess their movements, thus furnishing additional insights to fuel their subsequent growth. This article introduces a novel multimodal information fusion evaluation approach, combining sound information and movement data to address the challenge of evaluating students' learning status in college music instruction. The proposed framework leverages Internet of Things (IoT) technology, utilizing strategically positioned microphones and cameras within the local area network to accomplish data acquisition. Sound feature extraction is accomplished through the employment of Mel-scale frequency cepstral coefficients (MFCC), while the OpenPose framework in deep learning and convolutional neural networks (CNN) is harnessed to extract action features during students' performances. Subsequently, the fusion of feature layers is achieved through CNN, culminating in the evaluation of students' academic efficacy, facilitated by a fully connected network (FCN) and an activation function. In comparison to evaluations conducted by the teacher in the class, this approach achieves an impressive accuracy of 95.7% across the three categories of Excellent, Good, and Failed students' evaluation processes. This breakthrough offers novel insights for the future of music teaching and interactive class evaluations while expanding the horizons of multimodal information fusion methods' applications.