Artificial Intelligence Approaches in Music Audio Analysis

Abstract

As people’s pursuit of art gradually increases, the forms and types of music have become increasingly diverse. In the process of exploring different types of music, the demand for music audio analysis has gradually increased. With the continuous development of artificial intelligence technology, machine learning, deep learning and other methods have gradually entered the public eye with their efficient data processing capabilities. Therefore, artificial intelligence methods in music audio analysis have gradually become the focus of research. At present, the processing mode based on traditional features such as Mel frequency cepstral coefficients (MFCC) combined with machine learning methods has been widely adopted, but there are still certain limitations in feature expression ability and classification accuracy. In recent years, endto-end deep learning methods have shown stronger adaptability and accuracy by automatically extracting features for classification and recognition, promoting the advancement of pure music audio analysis technology. This article aims to provide theoretical support and practical guidance for researchers in related fields by organizing the application of artificial intelligence methods in pure music audio analysis, comparing and analyzing the advantages and disadvantages of various methods, and promoting the sustainable development and technological innovation of this field.