Composer Classification Using Maximum Probability Partitioning Based on Compression Principles

Abstract

Music classification is a fundamental task in the field of Music Information Retrieval. This paper focuses on composer classification, a specific task within music classification. Compressive techniques are commonly employed in such music classification tasks. In this study, we propose a method to apply the Computing Information Quantity using Maximum Probability partitioning to music classification. To evaluate the effectiveness of our proposed method, we perform composer classification, specifically distinguishing between Haydn and Mozart, who are well-known for their stylistic similarities. The experimental results demonstrate that our proposed approach outperforms traditional compression-based classification methods. Furthermore, we compare our method with non-compressive techniques, discussing the significance of feature extraction methods. Our proposed method is a parameter-free classification approach that does not require domain-specific musical expertise or feature extraction based on such expertise.