Comparative Analysis of Content-Based and Context-Based Similarity on Musical Data

Similarity measurement between two musical pieces is a hard problem. Humans perceive such similarity by employing a large amount of contextually semantic information. Commonly used content-based methodologies rely on information that includes little or no semantic information, and thus are reaching a performance “upper bound”. Recent research pertaining to contextual information assigned as free-form text (tags) in social networking services has indicated tags to be highly effective in improving the accuracy of music similarity. In this paper, we perform a large scale (20k real music data) similarity measurement using mainstream content and context methodologies. In addition, we test the accuracy of the examined methodologies against not only objective metadata but real-life user listening data as well. Experimental results illustrate the conditionally substantial gains of the context-based methodologies and a not so close match these methods with the real user listening data similarity.