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.