Computing Information Quantity as Similarity Measure for Music Classification Task
This work addresses the need for reproducible and scalable methods in music information retrieval, though it is incremental as it builds on existing dissimilarity measures.
The paper tackled the problem of composer estimation in music classification by proposing a new similarity measure based on information quantity, which outperformed a compression-based method with 75 piano scores from five composers.
This paper proposes a novel method that can replace compression-based dissimilarity measure (CDM) in composer estimation task. The main features of the proposed method are clarity and scalability. First, since the proposed method is formalized by the information quantity, reproduction of the result is easier compared with the CDM method, where the result depends on a particular compression program. Second, the proposed method has a lower computational complexity in terms of the number of learning data compared with the CDM method. The number of correct results was compared with that of the CDM for the composer estimation task of five composers of 75 piano musical scores. The proposed method performed better than the CDM method that uses the file size compressed by a particular program.