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

Ankitha Sudarshan, Atharva Vikas Jadhav, Rohini Srihari

Keywords: Nattuvangam, Structured Beats, Low Resource, South Asian Music, Machine Learning

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.

Submission Number: 22