Onset detection of Tabla Strokes using LP Analysis

Gowriprasad., R and Kodukula, Sri Rama Murty (2020) Onset detection of Tabla Strokes using LP Analysis. In: 2020 International Conference on Signal Processing and Communications, SPCOM 2020, 19 July 2020through 24 July 2020, Bangalore.

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Onset detection is an important first step in music analysis. We propose a pre-processing scheme for improved onset detection of complex strokes of Indian percussion instruments with resonance characteristics. In this work, we explore the onset detection of Tabla (Indian percussion instrument) strokes. The resonance characteristics of tabla strokes poses challenges to onset detection. In such cases, the energy-based and spectral flux-based onset detectors are often inaccurate on the raw signal. 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, spectral flux based and the state of the art Machine Learning based onset 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. © 2020 IEEE.

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IITH Creators:
IITH CreatorsORCiD
Kodukula, Sri Rama Murtyhttps://orcid.org/0000-0002-6355-5287
Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Hilbert envelope; Linear prediction; Music analysis; Onset detection; Percussion instruments; Pre-processing; Resonance characteristic; State of the art
Subjects: Electrical Engineering
Divisions: Department of Electrical Engineering
Depositing User: . LibTrainee 2021
Date Deposited: 16 Nov 2022 05:32
Last Modified: 16 Nov 2022 05:32
URI: http://raiith.iith.ac.in/id/eprint/11288
Publisher URL: http://doi.org/10.1109/SPCOM50965.2020.9179587
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