The Collision and Fusion of Minority Music and Popular Music in Multimedia Network Real-Time Video Processing

Abstract

The integration of minority music and popular music in multimedia network real-time video processing is being revolutionized by constant advancements in information technology (IT). The rapid development of data technology and artificial intelligence (AI) has introduced new interdisciplinary concepts to music education. This research presents a novel approach to music education, leveraging machine learning techniques. A comprehensive dataset, documenting students' diverse interests and activities, was analyzed and filtered using a Gaussian kernel filter. Subsequently, feature extraction was performed using a Boltzmann spatio convolutional neural network (GKF_BSCNN). Experimental investigations were conducted to evaluate the approach in terms of random accuracy, Area Under the Curve (AUC), precision, and recall across various student datasets. The proposed method demonstrated significant efficacy, achieving 97% random accuracy, 94% precision, 89% recall, and 40% AUC. This approach not only provides valuable assessment results with broad applicability but also mitigates the subjectivity inherent in traditional evaluation methods.