Low-Resource Rhythm Learning of South Asian Beat Structures: Machine Learning Approaches to Nattuvangam

Ankitha Sudarshan, Atharva Vikas Jadhav, Rohini Srihari
Proceedings of Machine Learning Research, PMLR 303:1-17, 2026.

Abstract

Semantic representations of rhythmic structures are important for AI-driven music generation and choreography. South Asian classical dance, such as Bharatanatyam, relies on intricate rhythms that guide choreography and improvisation. These rhythms are expressed through Nattuvangam, a vocal and percussive form that uses rhythmic syllables (Solkattus) and cymbal cues (Talam). Despite its pedagogical importance, Nattuvangam is rarely documented in digital form, which limits systematic study and teaching. We present the first curated dataset of Nattuvangam recordings that capture diverse Solkattu patterns and cyclic Talam structures. Each clip is analyzed using handcrafted and learned features, including onset envelopes, inter-onset intervals, tempograms, and Mel-spectrogram embeddings. These representations allow machine learning models to identify, cluster, and retrieve rhythmic motifs across performances. The dataset serves as a pedagogical tool and supports computational exploration of Solkattu patterns in relation to Talam, revealing the structural principles underlying Nattuvangam. This work establishes a foundation for studying Nattuvangam as both a standalone and performative art form, bridging cultural teaching with AI-based rhythm analysis in low-resource contexts.

Cite this Paper

BibTeX
@InProceedings{pmlr-v303-sudarshan26a, title = {Low-Resource Rhythm Learning of South Asian Beat Structures: Machine Learning Approaches to Nattuvangam}, author = {Sudarshan, Ankitha and Jadhav, Atharva Vikas and Srihari, Rohini}, booktitle = {Proceedings of Machine Learning Research}, pages = {1--17}, year = {2026}, editor = {Herremans, Dorien and Bhandari, Keshav and Roy, Abhinaba and Colton, Simon and Barthet, Mathieu}, volume = {303}, series = {Proceedings of Machine Learning Research}, month = {26 Jan}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v303/main/assets/sudarshan26a/sudarshan26a.pdf}, url = {https://proceedings.mlr.press/v303/sudarshan26a.html}, abstract = {Semantic representations of rhythmic structures are important for AI-driven music generation and choreography. South Asian classical dance, such as Bharatanatyam, relies on intricate rhythms that guide choreography and improvisation. These rhythms are expressed through Nattuvangam, a vocal and percussive form that uses rhythmic syllables (Solkattus) and cymbal cues (Talam). Despite its pedagogical importance, Nattuvangam is rarely documented in digital form, which limits systematic study and teaching. We present the first curated dataset of Nattuvangam recordings that capture diverse Solkattu patterns and cyclic Talam structures. Each clip is analyzed using handcrafted and learned features, including onset envelopes, inter-onset intervals, tempograms, and Mel-spectrogram embeddings. These representations allow machine learning models to identify, cluster, and retrieve rhythmic motifs across performances. The dataset serves as a pedagogical tool and supports computational exploration of Solkattu patterns in relation to Talam, revealing the structural principles underlying Nattuvangam. This work establishes a foundation for studying Nattuvangam as both a standalone and performative art form, bridging cultural teaching with AI-based rhythm analysis in low-resource contexts.} }
Endnote
%0 Conference Paper %T Low-Resource Rhythm Learning of South Asian Beat Structures: Machine Learning Approaches to Nattuvangam %A Ankitha Sudarshan %A Atharva Vikas Jadhav %A Rohini Srihari %B Proceedings of Machine Learning Research %C Proceedings of Machine Learning Research %D 2026 %E Dorien Herremans %E Keshav Bhandari %E Abhinaba Roy %E Simon Colton %E Mathieu Barthet %F pmlr-v303-sudarshan26a %I PMLR %P 1--17 %U https://proceedings.mlr.press/v303/sudarshan26a.html %V 303 %X Semantic representations of rhythmic structures are important for AI-driven music generation and choreography. South Asian classical dance, such as Bharatanatyam, relies on intricate rhythms that guide choreography and improvisation. These rhythms are expressed through Nattuvangam, a vocal and percussive form that uses rhythmic syllables (Solkattus) and cymbal cues (Talam). Despite its pedagogical importance, Nattuvangam is rarely documented in digital form, which limits systematic study and teaching. We present the first curated dataset of Nattuvangam recordings that capture diverse Solkattu patterns and cyclic Talam structures. Each clip is analyzed using handcrafted and learned features, including onset envelopes, inter-onset intervals, tempograms, and Mel-spectrogram embeddings. These representations allow machine learning models to identify, cluster, and retrieve rhythmic motifs across performances. The dataset serves as a pedagogical tool and supports computational exploration of Solkattu patterns in relation to Talam, revealing the structural principles underlying Nattuvangam. This work establishes a foundation for studying Nattuvangam as both a standalone and performative art form, bridging cultural teaching with AI-based rhythm analysis in low-resource contexts.
APA
Sudarshan, A., Jadhav, A.V. & Srihari, R.. (2026). Low-Resource Rhythm Learning of South Asian Beat Structures: Machine Learning Approaches to Nattuvangam. Proceedings of Machine Learning Research, in Proceedings of Machine Learning Research 303:1-17 Available from https://proceedings.mlr.press/v303/sudarshan26a.html.

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