Structural Segmentation and Labelling of Tabla Solo Performances

Abstract

Tabla is a North Indian percussion instrument that serves as both an accompaniment and a captivating solo instrument. Tabla solo is intricate and elaborate, exhibiting rhythmic evolution through a sequence of homogeneous sections marked by shared rhythmic characteristics. Each section has a specific structure and name associated with it. Tabla learning and performance in the Indian subcontinent is based on stylistic schools called gharānās. Several compositions by various composers from different gharānās are played in each section. This paper addresses the task of segmenting the tabla solo concert into musically meaningful sections. We then assign suitable section labels and recognise gharānās from the sections. We have curated a diverse collection of more than 38 hours of solo tabla recordings for the task. We motivate the problem and present different challenges and facets of the tasks. Inspired by the distinct musical properties of tabla solo, we compute several rhythmic and timbral features for the segmentation task. This work explores the approach of automatically locating the significant changes in the rhythmic structure by analysing local self-similarity in an unsupervised manner. We also explore the supervised random forest and a convolutional neural network trained on hand-crafted features. Both supervised and unsupervised approaches are also tested on a set of held-out recordings. Segmentation of an audio piece into its structural components and labelling is crucial to many music information retrieval applications like repetitive structure finding, audio summarisation, and fast music navigation. This work enables a comprehensive musical understanding and description of tabla solo concerts.