Survey of Research on Automatic Music Annotation and Classification Methods

Abstract

Music is one of the most popular forms of art and entertainment, and it is an artistic language to express or entrust people??s feelings. However, with the rapid increase of digital music, it is very difficult to manage and filter music through shallow information. As an effective means to organize massive music and enrich the music information, automatic music annotation can overcome the semantic gap in music information retrieval, improve music information, make music more intuitive in expression, and promote the in-depth research of music information retrieval tasks such as music classification, music recommendation, and instrument identification. The current automatic music annotation mainly focuses on solving two problems: feature extraction and model selection. Combined with the current research focus, this paper expounds the relevant knowledge of automatic music annotation. This paper systematically sorts out various audio feature representation and feature extraction methods in the field of music automatic annotation, and conducts quantitative and qualitative analysis of each extraction method. This paper summarizes the related research results in this field, and focuses on the differences between different model methods from the perspectives of machine learning and deep learning. The commonly used datasets and performance evaluation indicators are introduced, the characteristics of different datasets are summarized, and the evaluation indicators are classified and analyzed. Finally, the difficulties and challenges faced by the research in the field of music automatic annotation are pointed out and the future is prospected.

Keywords

• music information retrieval; automatic music annotation; feature extraction; deep learning