Structural Segmentation and Labeling of Tabla Solo Performances

TL;DR

This paper develops methods for automatically segmenting and labeling tabla solo performances into meaningful sections, recognizing gharana styles, using rhythmic and timbral features with supervised and unsupervised machine learning approaches.

Contribution

It introduces a novel dataset of over 38 hours of recordings and explores both supervised and unsupervised techniques for structural segmentation and gharana recognition in tabla solos.

Findings

Unsupervised self-similarity analysis effectively detects rhythmic changes.

Supervised models achieve high accuracy in segment labeling.

The approach enhances music information retrieval for Indian classical music.

Abstract

Tabla is a North Indian percussion instrument used as an accompaniment and an exclusive instrument for solo performances. 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 gharana-s. Several compositions by various composers from different gharana-s 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 recognize gharana-s from the sections. We present a diverse collection of over 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 analyzing local self-similarity in an unsupervised manner. We also explore 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 labeling is crucial to many music information retrieval applications like repetitive structure finding, audio summarization, and fast music navigation. This work helps us obtain a comprehensive musical description of the tabla solo concert.