  A Dhrupad vocal concert comprises a composition section that is interspersed
with improvised episodes of increased rhythmic activity involving the
interaction between the vocals and the percussion. Tracking the changing
rhythmic density, in relation to the underlying metric tempo of the piece, thus
facilitates the detection and labeling of the improvised sections in the
concert structure. This work concerns the automatic detection of the musically
relevant rhythmic densities as they change in time across the bandish
(composition) performance. An annotated dataset of Dhrupad bandish concert
sections is presented. We investigate a CNN-based system, trained to detect
local tempo relationships, and follow it with temporal smoothing. We also
employ audio source separation as a pre-processing step to the detection of the
individual surface densities of the vocals and the percussion. This helps us
obtain the complete musical description of the concert sections in terms of
capturing the changing rhythmic interaction of the two performers.
