Carnatic Music is a South Indian art and devotional musical practice in which melodic patterns (motifs and phrases), known as sañcāras, play a crucial structural and expressive role. We demonstrate how the combination of transposition invariant features learnt by a Complex Autoencoder (CAE) and predominant pitch tracks extracted using a Frequency-Temporal Attention Network (FTANet) can be used to annotate and group regions of variable-length, repeated, melodic patterns in audio recordings of multiple ...
Carnatic Music is a South Indian art and devotional musical practice in which melodic patterns (motifs and phrases), known as sañcāras, play a crucial structural and expressive role. We demonstrate how the combination of transposition invariant features learnt by a Complex Autoencoder (CAE) and predominant pitch tracks extracted using a Frequency-Temporal Attention Network (FTANet) can be used to annotate and group regions of variable-length, repeated, melodic patterns in audio recordings of multiple Carnatic Music performances. These models are trained on novel, expert-curated datasets of hundreds of Carnatic audio recordings and the extraction process tailored to account for the unique characteristics of sañcāras in Carnatic Music. Experimental results show that the proposed method is able to identify 54% of all sañcāras annotated by a professional Carnatic vocalist. Code to reproduce and interact with these results is available online.
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