  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.
