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

Abstract

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