Automatic Gharana Recognition from Audio music recordings of Tabla Solo performances

Gowriprasad, R and Kodukula, Sri Rama Murty (2019) Automatic Gharana Recognition from Audio music recordings of Tabla Solo performances. Masters thesis, Indian institute of technology Hyderabad.

Text
Mtech_Thesis_TD1446_2019.pdf
Restricted to Repository staff only until 4 July 2023.
Download (3MB) | Request a copy

Abstract

Tabla is a percussion instrument in Hindustani music (North Indian music tradition). Tabla learning and performance in the Indian subcontinent is based on stylistic schools called the ghar¯an¯as. This thesis aims to explore the problem of Tabla ghar¯an¯a recognition from solo tabla recordings by searching characteristic tabla phrases in these recordings. The concept of rhythm in Indian music, the instrument tabla and its ghar¯an¯as are briefly explained. At first, our work explores the onset detection of Tabla stokes, then deals with automatic ghar¯an¯a recognition and the final part focuses on tabla critic. Relevant research related to musical onset detection, segmentation, transcription and string search methods are reviewed and commented. Onset detection is an important first step in music analysis. We analyze the resonance characteristics of the tabla strokes, motivating the challenge in designing an onset detection algorithm. We propose an onset detection algorithm addressing these challenges using Linear Prediction (LP) analysis and Hilbert envelope (HE) in tandem. Tabla signal is modeled using LP, and its residual highlights the onset time instances very well. Unipolar nature of HE on top of LP residual further enhances the onset instances. Onset detection is performed using energy based and spectral flux based detectors on the Hilbert envelope of LP residual (HELP). Experiments were performed on tabla solo played at various tempi, and the results show that the HELP based approach provides 12% relative improvement in F-measures compared to the performance on raw tabla signal. Tabla learning and performance in the Indian subcontinent is based on stylistic schools called the ghar¯an¯as. Each ghar¯an¯a is characterized by its unique style of playing technique, dynamics of tabla strokes, improvisations and compositional patterns (signature patterns). Ghar¯an¯a identification helps in characterizing tabla performances and provides valuable information for analysis of solo tabla recordings. In this paper, we explore an approach for ghar¯an¯a recognition from solo tabla recordings by searching characteristic tabla phrases in these recordings. The tabla phrases are modeled as sequences of bols (onomatopoeic mnemonic syllables representing tabla strokes) and characteristic phrases from the ghar¯an¯a compositions are chosen as query patterns. To identify these b¯ol query patterns in a tabla solo audio recording, the recording is automatically transcribed into a b¯ols sequence using Hidden Markov Models (HMM). Rough Longest Common Sub-sequence (RLCS) approach is used to search for the query pattern instances. A novel decision rule is proposed to recognize the ghar¯an¯a from the matches. Our experiments on three major tabla ghar¯an¯as (Dilli, Lucknow, Banaras) show 79% recognition accuracy.

[error in script]

IITH Creators: