Tabla Gharānā Recognition from Tabla Solo Recordings

Abstract

Tabla is a percussion instrument in North Indian music tradition. Teaching practices and performances of tabla are based on stylistic schools called gharana-s. Gharana-s are characterized by their unique playing technique, finger posture, improvisations, and compositional patterns (signature patterns). Recognizing the gharana information from a tabla performance is hence helpful to characterize the performance. In this paper, we explore an approach for gharana recognition from solo tabla recordings by searching for the characteristic tabla phrases in these recordings. The tabla phrases are modeled as sequences of strokes, and characteristic phrases from the gharana compositions are chosen as query patterns. The recording is automatically transcribed into a syllable sequence using Hidden Markov Models (HMM). The Rough Longest Common Subsequence (RLCS) approach is used to search for the query pattern instances. A decision rule is proposed to recognize the gharana from the patterns.