Structure and Automatic Segmentation of Dhrupad Vocal Bandish Audio

TL;DR

This paper presents a method for automatically segmenting Dhrupad vocal performances by detecting changes in rhythmic density, utilizing CNN-based tempo detection, temporal smoothing, and source separation to analyze vocal and percussion interactions.

Contribution

It introduces an annotated dataset and a novel CNN-based approach combined with source separation for detailed rhythmic analysis of Dhrupad performances.

Findings

Effective detection of rhythmic density changes in Dhrupad concerts.

Improved segmentation accuracy using source separation.

Captures interaction between vocals and percussion.

Abstract

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.