Svara-forms and coarticulation in carnatic music: an investigation using deep clustering

Nuttall, Thomas; Serra, Xavier; Pearson, Lara

doi:https://dx.doi.org/10.1145/3660570.3660580

Svara-forms and coarticulation in carnatic music: an investigation using deep clustering

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Citation

Nuttall T, Serra X, Pearson L. Svara-forms and coarticulation in carnatic music: an investigation using deep clustering. In: Weigl DM, editor. DLfM '24: Proceedings of the 11th International Conference on Digital Libraries for Musicology; 2024 June 27; Stellenbosch, South Africa. New York: ACM; 2024. p. 15-22. DOI: 10.1145/3660570.3660580

Abstract

Across musical genres worldwide, there are many styles where the shortest conceptual units (e.g., notes) are often performed with ornamentation rather than as static pitches. Carnatic music, a style of art music from South India, is one example. In this style, ornamentation can include slides and wide oscillations that hardly rest on the theoretical pitch implied by the svara (note) name. The highly ornamented and oscillatory qualities of the style, in which the same svara may be performed in several different ways, means that transcription from audio to symbolic notation is a challenging task. However, according to the grammar of the Carnatic style, there are a limited number of ways that a svara may be realized in a given rāga (melodic framework), and these ways depend to some extent on immediate melodic context and svara duration. Therefore, in theory, it should be possible to identify not only svaras but also the various characteristic ways that any given svara is performed - referred to here as ‘svara-forms’. In this paper we present a dataset of 1,530 manually created svara annotations in a single performance of a composition in rāga Bhairavi, performed by the senior Carnatic vocalist Sanjay Subrahmanyan. We train a recurrent neural network and sequence classification model, DeepGRU, on the extracted pitch time series of the predominant vocal melody corresponding to these annotations to learn an embedding that classifies svara label with 87.6% test accuracy. We demonstrate how such embeddings can be used to cluster svaras that have similar forms and hence elucidate the distinct svara-forms that exist in this performance, whilst assisting in their automatic identification. Furthermore, we compare the melodic features of our 54 svara-form clusters to illustrate their unique character and demonstrate the dependency between these cluster allocations and the immediate melodic context in which these svaras are performed.

Description

Comunicació presentada a 11th International Conference on Digital Libraries for Musicology, celebrada el 27 de juny de 2024 a Stellenbosch, Sud-àfrica.