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Tabla is a North Indian percussion instrument used as an accompaniment and anexclusive instrument for solo performances. Tabla solo is intricate andelaborate, exhibiting rhythmic evolution through a sequence of homogeneoussections marked by shared rhythmic characteristics. Each section has a specificstructure and name associated with it. Tabla learning and performance in theIndian subcontinent is based on stylistic schools called gharana-s. Severalcompositions by various composers from different gharana-s are played in eachsection. This paper addresses the task of segmenting the tabla solo concertinto musically meaningful sections. We then assign suitable section labels andrecognize gharana-s from the sections. We present a diverse collection of over38 hours of solo tabla recordings for the task. We motivate the problem andpresent different challenges and facets of the tasks. Inspired by the distinctmusical properties of tabla solo, we compute several rhythmic and timbralfeatures for the segmentation task. This work explores the approach ofautomatically locating the significant changes in the rhythmic structure byanalyzing local self-similarity in an unsupervised manner. We also exploresupervised random forest and a convolutional neural network trained onhand-crafted features. Both supervised and unsupervised approaches are alsotested on a set of held-out recordings. Segmentation of an audio piece into itsstructural components and labeling is crucial to many music informationretrieval applications like repetitive structure finding, audio summarization,and fast music navigation. This work helps us obtain a comprehensive musicaldescription of the tabla solo concert.

Structural Segmentation and Labeling of Tabla Solo Performances