5 papers · 1 filter
Confidence-Guided Markov Weighting for Semi-Supervised Tabla Stroke Transcription
Rahul Bapusaheb Kodag, Vipul Arora
Tabla Stroke Transcription (TST) converts tabla audio into symbolic stroke sequences, but the scarcity of annotated recordings makes fully supervised training challenging. We propo…
Weakly Supervised Tabla Stroke Transcription via an Adaptive Dynamic Rhythm Language Model (ADRM)
Rahul Bapusaheb Kodag, Vipul Arora
Tabla Stroke Transcription (TST) is central to the analysis of rhythmic structure in Hindustani music, yet it remains challenging due to complex and dynamic rhythmic organization a…
Weakly Supervised Tabla Stroke Transcription via TI-SDRM: A Rhythm-Aware Lattice Rescoring Framework
Rahul Bapusaheb Kodag, Vipul Arora
Tabla Stroke Transcription (TST) is central to the analysis of rhythmic structure in Hindustani classical music, yet remains challenging due to complex rhythmic organization and th…
Meta-learning-based percussion transcription and identification from low-resource audio
Rahul Bapusaheb Kodag, Vipul Arora
This study introduces a meta-learning-based approach for low-resource Tabla Stroke Transcription (TST) and identification in Hindustani classical music. Using Model-Ag…
A System for Automatic Identification and Generation
Rahul Bapusaheb Kodag, Himanshu Jindal, Vipul Arora
In Hindustani classical music, the tabla plays an important role as a rhythmic backbone and accompaniment. In applications like computer-based music analysis, learning singing, and…