Leveraging melodic context for improved Svara representation

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• dc.contributor.author Nuttall, Thomas
• dc.contributor.author Vijayan, Vivek
• dc.contributor.author Serra, Xavier
• dc.contributor.author Pearson, Lara
• dc.date.accessioned 2025-11-19T08:32:07Z
• dc.date.available 2025-11-19T08:32:07Z
• dc.date.issued 2025
• dc.description.abstract For the South Indian musical tradition known as Carnatic music, embeddings of svara (note) pitch time series have proven useful for tasks such as svara classification and performance analysis. In this paper, we extend an existing embedding method by incorporating findings from musicological research on the relationship between the performance of a svara and its immediate melodic context, in order to improve the learning of these embedding models. We present a context-aware GRUbased model, adapting the existing DeepGRU architecture to encode both svara and its surrounding melodic context, before combining them via a co-attention mechanism prior to classification. For a ground truth dataset of 2,077 expert svara annotations across two performances in r¯aga Bhairavi, we observe that the inclusion of melodic context leads to a 6.6% absolute increase in F1 score for svara label classification (from 78.3% to 84.9%), and an 7.8% absolute increase (from 59.9% to 67.7%) for classification of svara-form: sub-svara clusters that capture gamaka (ornamentation) variations in the performed svara.
• dc.description.sponsorship This research was carried out as part of the "IA y Música: Cátedra en Inteligencia Artificial y Música" (TSI-100929-2023-1), funded by the Secretaría de Estado de Digitalización e Inteligencia Artificial, and the European Union-Next Generation EU, under the program Cátedras ENIA 2022 para la creación de cátedras universidad-empresa en IA.
• dc.format.mimetype application/pdf
• dc.identifier.citation Nuttall T, Vijayan V, Serra S, Pearson L. Leveraging melodic context for improved Svara representation. In: The 17th International Symposium on Computer Music Multidisciplinary Research CMMR 2025; 2025 Nov 3-7; London, UK. MArseille: Laboratory PRISM; 2025. p 185-96. DOI: 10.5281/zenodo.17497900
• dc.identifier.doi 10.5281/zenodo.17497900
• dc.identifier.isbn 9791097498061
• dc.identifier.uri http://hdl.handle.net/10230/71932
• dc.language.iso eng
• dc.publisher Laboratory PRISM
• dc.relation.ispartof The 17th International Symposium on Computer Music Multidisciplinary Research CMMR 2025; 2025 Nov 3-7; London, UK. MArseille: Laboratory PRISM; 2025.
• dc.rights All copyrights remain with the authors. Creative Commons Attribution 4.0 International
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.rights.uri http://creativecommons.org/licenses/by/4.0
• dc.subject.keyword Representation learning
• dc.subject.keyword Time series analysis
• dc.subject.keyword Carnatic music
• dc.subject.keyword Coarticulation
• dc.title Leveraging melodic context for improved Svara representation
• dc.type info:eu-repo/semantics/conferenceObject
• dc.type.version info:eu-repo/semantics/publishedVersion