In this paper, we propose a way to improve the compression based dissimilarity measure, CDM. We propose to use a modified value of the file size, where the original CDM uses an unmodified file size. Our application is a music score analysis. We have chosen piano pieces from five different composers. We have selected 75 famous pieces (15 pieces for each composer). We computed the distances among all pieces by using the modified CDM. We use the K-nearest neighbor method when we estimate the composer of each piece of music. The modified CDM shows improved accuracy. The difference is statistically significant.
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.
Compression-based Dissimilarity Measure (CDM) is reported to work well in classifying strings without clues. However, CDM depends on the compression program, and its theoretical background is unclear. In this paper, we propose to replace CDM with the computation of information quantity. Since CDM only uses compressed size, our approach uses the value of information quantity of maximum probability partitioning of string instead of file size. We find this approach is more effective. Then, CDM and the proposed method were applied to publicly available time series data. In addition to the careful implementation of computation using suffix arrays, we also find this approach more efficient.
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