Improving Rhythmic Similarity Computation by Beat Histogram Transformations

Rhythmic descriptors are often utilized for semantic music classification, such as genre recognition or tempo detection. Several algorithms dealing with the extraction of rhythmic information from music signals were proposed in literature. Most of them derive a so-called beat histogram by auto-correlating a representation of the temporal envelope of the music signal. To circumvent the problem of tempo dependency, post-processing via higher-order statistics has been reported. Tests concluded, that these statistics are still tempo dependent to a certain extent. This paper describes a method, which transforms the original auto-correlated envelope into a tempo-independent rhythmic feature vector by multiplying the lag-axis with a stretch factor. This factor is computed with a new correlation technique which works in the logarithmic domain. The proposed method is evaluated for rhythmic similarity, consisting of two tasks: One test with manually created rhythms as proof of concept and another test using a large realworld music archive.