Towards Multi-Purpose Spectral Rhythm Features: An Application to Dance Style, Meter and Tempo Estimation

This paper addresses the extraction of multipurpose spectral rhythm features that simultaneously tackle a variety of rhythm analysis tasks, namely, dance style classification, meter estimation, and tempo estimation. The term spectral rhythm features emanates from the origin of the extracted features, which is the periodicity function (PF), a spectral representation that encapsulates the salience of the rhythm frequencies. Two dimensionality reduction techniques applied on the PF to extract expressive and compact features are compared, namely, a linear transformation resulting from Principal Component Analysis and a nonlinear mapping derived from a Restricted Boltzmann Machine. Subsequently, the derived features were used as input to an SVM classifier for each task. Moreover, an additional method is proposed that reformulates the well-studied tempo estimation task as a combination of multiple binary classification sub-problems. Evaluation was performed on a large number of datasets demonstrating that the same set of features learned from the PF provide a robust rhythmic representation that achieved comparable results to the current state-of-the-art methods for the aforementioned tasks.