A supervised approach to hierarchical metrical cycle tracking from audio music recordings

Abstract

A supervised approach to metrical cycle tracking from audio is presented, with a main focus on tracking the tala, the hierarchical cyclic metrical structure in Carnatic music. Given the tala of a piece, we aim to estimate the aksara (lowest metrical pulse), the aksara period, and the sama (first pulse of the tala cycle). Starting with percussion enhanced audio, we estimate the aksara pulse period from a tempogram computed using an onset detection function. A novelty function is computed using a self similarity matrix constructed using frame level audio features. These are then used to estimate possible aksara and sama candidates, followed by a candidate selection based on periodicity constraints, which leads to the final estimates. The algorithm is tested on an annotated collection of 176 pieces spanning four different talas. Though applied to Carnatic music, the framework presented is general and can be extended to other music cultures with cyclical metrical structures.