Scale Transform in Rhythmic Similarity of Music

As a special case of the Mellin transform, the scale transform has been applied in various signal processing areas, in order to get a signal description that is invariant to scale changes. In this paper, the scale transform is applied to autocorrelation sequences derived from music signals. It is shown that two such sequences, when derived from similar rhythms with different tempo, differ mainly by a scaling factor. By using the scale transform, the proposed descriptors are robust to tempo changes, and are specially suited for the comparison of pieces with different tempi but similar rhythm. As music with such characteristics is widely encountered in traditional forms of music, the performance of the descriptors in a classification task of Greek traditional dances and Turkish traditional songs is evaluated. On these datasets accuracies compared to non-tempo robust approaches are improved by more than 20%, while on a dataset of Western music the achieved accuracy improves compared to previously presented results.