A Multimodal Approach to Song-Level Style Identification in Pop/Rock Using Similarity Metrics

This paper presents a multimodal approach to style identification in pop/rock music. Considering the intuitive feelings of similarity from the listener's perspective, this study focuses on features that are computed using similarity metrics for melodies, harmonies, and audio signals for style identification. Support vector machine is used as a binary classifier to determine if two songs are created by the same artist given their similarity distances in the three aspects. Experiments are conducted using songs of four well-known pop/rock bands from 6 albums. The preliminary result shows that the approach achieves the best result in correct rate of 85% using only seven similarity metrics.