A passive approach to evaluating Mridangam transcription via perceptual experiment

Kaustuv Ganguli, Gautham Lali, Askhay Anantapadmanabhan, Carlos Guedes

Research output: Contribution to conferencePaperpeer-review


Analysis and characterization of percussive instruments using computational methods have recently got attention both in the context of Western and non-Western repertoire, specifically on automatic transcription of tabla and mridangam strokes (Anantapadmanabhan et al., 2014; Guedes et al., 2018). The transcription approach, however, is limited by its dependency on prior knowledge about the specific modes of the instrument. Another concern is the absence of a unique mapping between the strokes and their nomenclature in the vocabulary. The same stroke is often uttered differently in the konakkol vocalization based on contextual variations or grammatical impositions. Most notably, even an expert musician is often unable to resolve such ambiguities on isolated presentation of a stroke but uses contextual cues to possibly interpolate within and across phrases. Our previous work (Ganguli et al., 2020; 2021) addressed this problem by proposing a combination of acoustic and semantic approaches for the contextual transcription of mridangam strokes. Grammatically-accurate labels for the transcribed strokes were obtained through the learned language model as knowledge-constraints.
Original languageEnglish (US)
StatePublished - 2021
Eventnternational Conference of Music Perception and Cognition: ICMPC-ESCOM - Hyderabad, India, Hyderabad, India
Duration: Jul 28 2021Jul 31 2021


Conferencenternational Conference of Music Perception and Cognition
City Hyderabad
Internet address


Dive into the research topics of 'A passive approach to evaluating Mridangam transcription via perceptual experiment'. Together they form a unique fingerprint.

Cite this